Reliability performance and maintenance
- A survey of failures in wind power systems
Master Thesis
by Johan Ribrant
Master Thesis written at KTH School of Electrical Engineering, 2005/2006
Supervisor:
Lina Bertling, KTH School of Electrical Engineering
Assistant supervisor:
Thomas Ackermann, KTH School of Electrical Engineering
Examiner:
Lina Bertling, KTH School of Electrical Engineering
XR-EE-EEK 2006:009
Reliability performance and maintenance – a survey of failures in wind power systems
ii
Reliability performance and maintenance – a survey of failures in wind power systems
Abstract
The wind power industry has expanded during the past few years and the growth has mainly
focused on a growing market and the development of larger wind turbines. Different designs
have emerged and the technical knowledge makes it possible to put wind turbines off shore.
The fast expansion of the wind power market has also come with some problems. The new
designs are not always fully tested, and the designed lifetime of 20 years is typically never
achieved until the next generation of turbines is erected.
The extreme conditions and the high loads that a wind turbine is exposed to makes the
coordination of maintenance an interesting issue. How much maintenance is needed? Are
there any ways of minimizing the maintenance and yet have a good availability for the wind
turbine? The technical availability of wind turbines is high, around 98%, but this is due to fast
and frequent service and not just because of good reliability or maintenance management.
The problem area for this thesis work, performed within the RCAM group at KTH School of
Electrical Engineering, is focused on the reliability for the components of the wind power
system. If the most critical components for the system can be identified, it will show in what
areas to focus when planning the maintenance for the system. If the condition of these critical
components can be supervised, the maintenance can be planned even further.
Investigations of failure statistics from four different sources reveal the reliability
performance of the different components within the wind turbine. The gearbox is found to be
the most critical as the downtime per failure is high in comparison to the other components in
the wind power turbine. The statistical data presented also show trends of higher and even
increasing failure frequency for bigger turbines compared to small turbines which have a
decreasing failure rate over the operational years.
Causes for failures to the gearbox are discussed and one of the major contributors to the
failure is alignment. If the alignment is incorrect the wear on the gear and the bearings will be
excessive and the lifetime of the gearbox will be reduced. To reduce the risk of a failure, the
monitoring of the gearbox is required. One way of monitoring the performance of the gearbox
is by using a condition monitoring system.
A Condition Monitoring System, CMS, is a tool for telling in what condition the components
in a system are. CMS are used today in many other applications but in the wind power
industry the CMS is relatively new. With CMS a prediction of impending failure is given for
each component, and therefore maintenance and repairs can then be better scheduled. The
CMS for the gearbox primarily measures vibrations but a supervision of the oil is also
necessary. The CMS used today are capable of detecting failures well in time prior to a failure
and they are even able to predict which component inside the gearbox is defective.
As a conclusion of this thesis work, it has been found that the gearbox is one of the most
critical components when it comes to which component that influences the downtime the
most. It is also shown that condition monitoring systems of today are able to supervise the
gearbox adequately. The theoretical implications of using condition based maintenance
together with condition monitoring systems shows great benefits and the overall conclusion is
that the use of CMS is beneficial when it comes to reducing the amount of failures of the
gearbox and also when it comes to scheduling the preventive maintenance.
iii
Reliability performance and maintenance – a survey of failures in wind power systems
iv
Reliability performance and maintenance – a survey of failures in wind power systems
Sammanfattning
De senaste åren har vindkraftsindustrin expanderat och tillväxten har främst varit inriktad mot
en växande marknad samt mot utvecklingen av större vindturbiner. Olika typer av design har
utvecklats och det tekniska kunnandet gör det nu möjligt att placera vindkraftverken till havs,
offshore. Den snabba utvecklingen av vindkraften har även bidragit med en del problem. The
nya turbinerna är inte alltid fullt testade och den förväntade livslängden på 20 år är aldrig
uppfylld innan nästa generation vindkraftverk är installerade.
De extrema förhållandena och de höga laster som ett vindkraftverk är utsatt för gör
samordningen av underhållet till en intressant fråga. Hur mycket underhåll behövs? Finns det
möjligheter att minimera underhållet och ändå ha en god tillgänglighet för vindkraftverket?
Den tekniska tillgängligheten för vindkraftverk är hög, omkring 98%, men detta är främst
beroende på snabbt och frekvent underhåll och inte på grund av bra tillförlitlighet eller bra
underhållsplanering.
Problemområdet för detta examensarbete, som är utfört inom RCAM-gruppen vid KTH
skolan för Elektroteknik, fokuserar på tillförlitligheten hos komponenter inom
vindkraftsystem. Om de mest känsliga kritiska komponenterna för systemet kan identifieras,
visar detta inom vilket område som underhållet behöver fokuseras. Om tillståndet för dessa
kritiska komponenter kan övervakas, kan det förebyggande underhållet planeras ytterligare.
En undersökning av felstatistik från fyra olika källor avslöjar tillförlitligheten hos de olika
komponenterna i vindkraftverket. Växellådan visar sig vara den mest kritiska komponenten då
hindertiden vid varje fel är hög i jämförelse med the andra komponenterna i
vindkraftsystemet. De statistiska data som presenteras visar också på trender av högre och
ökande felfrekvens för större turbiner jämfört med mindre turbiner som istället har minskande
felfrekvens över driftåren.
Orsaker till fel på växellådan diskuteras och ett av de stora bidragen till fel är upprikting, dvs.
att alla komponenter är injusterade mot varandra. Om uppriktningen är felaktig kommer
slitaget på växellåda och lager bli mer än förväntat och livslängden på växellådan blir
förkortad. För att förhindra risken för fel behövs övervakning av växellådan. Ett sätt att
övervaka växellådan är att använda ett tillståndsbaserat övervakningssystem, CMS (Condition
Monitoring System).
Ett tillståndsbaserat övervakningssystem, CMS, är ett verktyg för att avgöra i vilket tillstånd
komponenterna i systemet befinner sig i. CMS används idag inom flera områden men är
relativt ny inom vindkraft. CMS gör det möjligt att förutsäga när och vilken komponent som
är på väg att fela och därmed kan underhållet planeras bättre. CMS för växellådan mäter
huvudsakligen vibrationer men övervakning av växellådeoljan är minst lika viktig. De CMS
som används i dag är kapabla att detektera fel i god tid och de kan även förutsäga vilken
komponent i växellådan som är defekt.
Sammanfattningsvis har detta examensarbete funnit att växellådan är en av de mest kritiska
komponenterna när det gäller vilken komponent som påverkar hindertiden mest. Det har
också visats att dagens tillståndsövervakningssystem kan övervaka en växellåda. De
teoretiska resonemangen av att använda tillståndsbaserat underhåll tillsammans med dagens
övervakningssystem visar på stora fördelar och den sammanfattande slutsatsen är att
användandet av CMS är fördelaktigt när det gäller att reducera antalet hindertimmar för
växellådor samt när det gäller att planera det förebyggande underhållet.
v
Reliability performance and maintenance – a survey of failures in wind power systems
vi
Reliability performance and maintenance – a survey of failures in wind power systems
Acknowledgments
I would especially thank the following people for giving me valuable input and information:
Nils-Eric Carlstedt at Swedpower AB, for letting me use the information found in their
statistical database.
Anders Andersson at Vattenfall AB, for giving valuable information and for the guided tour at
Näsudden.
Per Erik Larsson at SKF Luleå, for useful information.
Hannele Holtinen at VTT in Finland, for valuable information and for the translation of the
incident report.
The personnel at Smöla, for providing information about their windfarm.
And finally the colleagues at RCAM, KTH school of Electrical Engineering.
vii
Reliability performance and maintenance – a survey of failures in wind power systems
viii
Reliability performance and maintenance – a survey of failures in wind power systems
Table of contents
Introduction .............................................................................................................................. 1
1
Background and problem discussion.......................................................................... 1
1.1
1.2
1.3
1.4
1.5
Thesis background ................................................................................................................. 1
Problem background .............................................................................................................. 2
Problem discussion ................................................................................................................ 3
Approach................................................................................................................................ 3
Thesis Overview .................................................................................................................... 4
Theory ....................................................................................................................................... 5
2
The basics of a wind power plant................................................................................ 5
2.1
2.2
2.3
3
Modeling of the system.......................................................................................................... 5
Choice of components............................................................................................................ 5
The components of the wind power system........................................................................... 6
Reliability theory ........................................................................................................ 11
3.1
3.2
3.3
3.4
4
Definitions ........................................................................................................................... 11
Probability distributions and their applications ................................................................... 11
The Alternating Renewal Process ........................................................................................ 13
Measurements ...................................................................................................................... 14
Maintenance methods ................................................................................................ 17
4.1
4.2
4.3
4.4
Corrective maintenance ....................................................................................................... 18
Preventive maintenance ....................................................................................................... 19
Comparison of maintenance methods .................................................................................. 21
Maintenance strategy ........................................................................................................... 22
Analysis ................................................................................................................................... 23
5
Survey of failures for wind power turbines ............................................................. 23
5.1
5.2
5.3
5.4
5.5
5.6
5.7
6
Access to statistical data ...................................................................................................... 23
Failure statistics ................................................................................................................... 25
Statistics from Sweden......................................................................................................... 26
Statistics from Finland ......................................................................................................... 37
Statistics from Germany ...................................................................................................... 43
Discussion about the reliability of the statistic data............................................................. 48
Conclusions on the findings in the statistical survey ........................................................... 49
Overview of the Gearbox........................................................................................... 51
6.1
6.2
6.3
6.4
6.5
6.6
7
Gearbox design .................................................................................................................... 51
Gearbox operating conditions .............................................................................................. 53
Gearbox development .......................................................................................................... 53
Gearbox wear and failures ................................................................................................... 54
Causes for gearbox failures.................................................................................................. 55
Conclusion on gearbox failures............................................................................................ 56
Condition Monitoring Systems ................................................................................. 57
7.1
7.2
7.3
7.4
7.5
Benefits of a Condition Monitoring System ........................................................................ 57
Insurance and CMS.............................................................................................................. 58
Condition monitoring in general .......................................................................................... 58
Condition monitoring for gearboxes .................................................................................... 59
Conclusions about condition monitoring for gearboxes ...................................................... 63
ix
Reliability performance and maintenance – a survey of failures in wind power systems
Closure..................................................................................................................................... 65
8
Conclusions and future work .................................................................................... 65
8.1
8.2
9
Conclusions.......................................................................................................................... 65
Future work.......................................................................................................................... 66
References ................................................................................................................... 67
9.1
9.2
Literature.............................................................................................................................. 67
Interviews............................................................................................................................. 69
Appendix 1 – Incident report from Sweden......................................................................... 70
Appendix 2 - CMS suppliers ................................................................................................. 71
x
Reliability performance and maintenance – a survey of failures in wind power systems
Introduction
1 Background and problem discussion
“In the last 20 years turbines have increased in power by a factor of 100, the cost of
energy has reduced, and the industry has moved from an idealistic fringe activity to
the edge of conventional power generation.” - European Wind Energy Association,
Wind Energy - The Facts, 2005 [4]
The wind power industry has expanded during the past few years and the growth has mainly
focused on a growing market and the development of larger wind turbines. Different designs
have emerged and the technical knowledge makes it now possible to put wind turbines off
shore.
The fast expansion of the wind power market has also come with some problems. The new
designs are not always fully tested, and the designed lifetime of 20 years is typically never
achieved until the next generation of turbines is erected. Some manufacturing failures have
been so extensive that turbine manufacturers nearly went bankrupt.
“Consider that a modern wind turbine operates for about 13 years in a design life of
20 and is almost always unattended. A motor vehicle, by comparison, is manned,
frequently maintained and its design life of about 150,000 kilometres is equivalent to
just four months of continuous operation.” - European Wind Energy Association,
Wind Energy - The Facts, 2005 [4]
The wind turbine is in several ways a unique power generating system as the power train
components are subject to highly irregular loading from turbulent wind conditions, and the
number of fatigue cycles experienced by the major structural components can be far greater
than for other rotating machines [4].
1.1 Thesis background
This thesis work is a part of the pre-study on reliability-centered maintenance for wind power
systems with focus on condition monitoring systems [1] performed within the RCAM group
at KTH, School of Electrical Engineering on behalf of Elforsk. Elforsk is an organization
owned by the Swedish power industry that encourages the industry to perform joint research
and development within electrical generation and distribution.
The long-term goal for that research project is to identify problem areas and possible solutions
for optimal maintenance management. The focus of the research project is condition
monitoring systems that could support the maintenance. This thesis work will examine one of
the underlying problems with the availability of the turbine and suggest a possible solution for
this kind of problem.
1
Reliability performance and maintenance – a survey of failures in wind power systems
1.2 Problem background
The extreme conditions and the high loads that a wind turbine is exposed to makes the
coordination of maintenance an interesting issue. How much maintenance is needed? Are
there any ways of minimizing the maintenance and yet having a good availability for the wind
turbine? These are issues that are discussed today within research and development as well as
within operations and maintenance for wind power plants.
The reasons for the underlying problems within the wind industry were somewhat cloudy
when the work with this thesis began. The technical availability of wind turbines is high,
around 98%, but this is due to fast and frequent service and not just because of good
reliability or maintenance management [1], [31]. It has also been known that the
manufacturers seldom reveal data about their products and even more rarely do they share
information about their failures, which is quite understandable.
A report from Elforsk shows that in recent years the amount of damage claims has increased
according to a report from a German insurance company. It is suggested that the reasons for
this development are the following [2]:
1. Technical reasons
o Insufficient prototype testing
o Excessively fast development
o Insufficient dimensioning and wrong selection of components
2. Operation and Maintenance reasons
o Bad documentation
o Lack of appropriate maintenance
o Lack of quality control
o Insufficient stocking of spare parts
The suggestion that some components are not fully developed and that the maintenance is not
appropriate is interesting, and motivated this work. New methods of how to predict the
maintenance that is needed have evolved with the introduction of condition monitoring
systems.
A Condition Monitoring System, CMS, is a tool for telling in what condition the components
in a system are. With this tool it is possible to predict when a component is likely to fail and
therefore it is also possible to schedule its replacement in advance.
Condition monitoring systems are used today in many other applications, e.g. the pulp and
paper industry [30]. In the wind power industry the CMS is relatively new. In 2001 a project
for examining the possibility of using a condition monitoring system was undertaken by
Elforsk in cooperation with Göteborgs Energi and SKF Nova. At that time no monitoring
system was available for a wind turbine and SKF Nova developed a system that was tested on
a Vestas V44 turbine. [3]
Today there are different condition monitoring system options available such as integrated
systems sold by the manufacturer or separate systems sold by companies such as for example
manufacturers of bearings. A typical owner of wind power turbines can have different
turbines from different manufacturers and needs a CMS that will be functional for all of them.
2
Reliability performance and maintenance – a survey of failures in wind power systems
The effect of these different systems is not yet thoroughly researched and this paper is an
effort to clarify some of the issues of condition monitoring within wind power systems.
1.3 Problem discussion
The problem area for this thesis work is focused on the reliability of the components of the
wind power system. If the most critical components for the system can be identified, it will
show in what areas to focus when planning the maintenance for the system.
By doing an in depth study of the failures one can find out which components fail, how often
they fail and if it is possible to measure the wear of the component and from this
measurement decide when to perform the maintenance. The wind power systems usually have
a high rate of availability but this is because of frequent maintenance [31]. Frequent
maintenance however, is obviously not a good and optimal solution. Preventive maintenance
at the right moment will save money for the owner of the wind power plant. Especially since
some wind power plants are situated at remote sites, for example offshore.
The problem discussion can be narrowed down into two major questions that will be clarified
and given an answer in this thesis work. These questions are:
1. What component or components are most critical in the wind turbine when it comes to
number of failures and the resulting downtime caused by these failures?
2. Is it possible to use a CMS to supervise these critical components and is CMS a
suitable tool for decreasing the amount of maintenance for the wind power system?
1.4 Approach
This thesis uses a quantitative approach as well as a qualitative approach. In a pre study
phase, the state-of-the-art and basic fundamentals are investigated through books and course
material used in wind power courses at the School of Electrical Engineering, KTH. The main
findings about the lifetime and failures of the components are based upon statistical data
which is analyzed with measurements used in reliability theory. The findings are then
supported by information found in articles, books and interviews related to the area of failures
within wind power systems.
•
•
Quantitative analysis - based on statistical data from Sweden, Finland and Germany.
Qualitative analysis - based on articles, internet resources, field trips, interviews and email correspondence.
3
Reliability performance and maintenance – a survey of failures in wind power systems
1.5 Thesis Overview
Chapter 1 - Background and problem discussion
This chapter gives an overview and a presentation of this thesis work and stipulates two
questions that are subject to investigation.
Chapter 2 – The basics of a wind power plant
The fundamentals of the components used in a wind power plant are presented. Different
ideas on how to solve the problem with power regulation is explained briefly.
Chapter 3 – Reliability theory
In this chapter main concepts and measurements within reliability theory are explained. Ways
of modelling the lifetime of a component are introduced along with ways of modelling the
wear and repair process of a system. The findings from reliability theory are later used as a
tool to extract failure rates and important key figures from the statistical data.
Chapter 4 – Maintenance methods
This chapter deals with different strategies for repair and maintenance. Different ways of how
to perform maintenance were encountered during the work within this thesis and these are
explained in this chapter. The basics from reliability theory are applied and aid in explaining
the differences between the strategies. The concept of condition monitoring is briefly
introduced to complement these strategies.
Chapter 5 – Survey of failures for wind power turbines
The theoretical measurements found within reliability theory are applied to statistical data
from three different countries. The findings from the statistical survey are analysed and
presented along with conclusions about the frequency of failure and the downtime. With the
conclusions found in this chapter, the first questions for this thesis work will be answered:
which component fails the most and which has the longest downtime?
Chapter 6 – Overview of the Gearbox
The statistical survey confirms that the gearbox is one of the most critical components for the
wind power turbine. This chapter explains more about the gearbox and about some design
terminology related to gearboxes.
Chapter 7 – Condition Monitoring Systems
In this chapter an explanation on how the monitoring system works is given along with a
description of techniques on how to monitor the gearbox. The chapter explains what these
systems are capable of and what type of measurements they perform. No advice will be given
as to which of the available systems is the best one, as a more thorough investigation would
be needed.
Chapter 8 – Conclusions and future work
A summary of all the findings together with topics and ideas for future work is described.
4
Reliability performance and maintenance – a survey of failures in wind power systems
Theory
2 The basics of a wind power plant
The function of a wind power system is to transform the kinetic energy in the wind into
electric energy. This is accomplished by letting the wind energy force an aerodynamic rotor to
turn. The wind energy is thus transformed into mechanical energy. The mechanical energy in
the form of a slow turning rotor shaft is geared up to a high-speed shaft which is connected to
a generator. Inside the generator the rotational mechanical energy is transformed into
electrical energy. The electric power output is then connected to the grid.
The basic function of the wind power system may sound easy but the system is still very
complex. The development within wind power has been extensive in recent years and
different concepts and construction designs have evolved. There has been a constant drive for
higher performance and a higher power output. In addition to the complexity of the business,
each manufacturer has basically chosen their own way of designing a wind turbine system.
The evolution process within the wind power business has changed the features of some of
the components, but the basic idea of turning wind energy into electrical energy via a
generator is still the same.
“Many developments and improvements have taken place since the
commercialization of wind technology in the early 1980s, but the basic architecture
of the mainstream design is little changed. Most of the wind turbines have upwind
rotors and are actively yawed to preserve alignment with wind direction.” –
European Wind Energy Association, Wind energy, the facts, 2005 [4]
The three-bladed rotor proliferates and, typically, has a separate front bearing with a low
speed shaft connected to a gearbox which provides an output speed suitable for a four-pole
generator [4].
2.1 Modeling of the system
The wind power system is a complex system and to do a better analysis a certain level of
modeling has to be made. When modeling a complicated system, a good approach is to divide
the system into smaller parts such as subsystems or components. In this case the whole plant
including structure and all electrical parts up to the grid connection will be viewed as the
system. The system consists of several complex parts that ought to be modeled as subsystems,
but as a first approach all the subsystems are modeled as components of the main system.
2.2 Choice of components
The selection of components for the description of the main system is not just an arbitrary
choice but a choice of what is useful in practice and where available data can be found. The
choice of which component should be used for modeling the whole system is based on
function and available information.
5
Reliability performance and maintenance – a survey of failures in wind power systems
2.2.1
A choice based on function
When describing a wind power system, a common way is to explain the main function by
dividing the system into a set of different components with different features, for example
brakes, tower, rotor blades etc. The different components are manufactured differently and are
easy to replace as modules in the system, hence it is convenient to view them as separate
components in the system.
2.2.2
A choice based on information
The second choice for which components to be used in the modeling of the system is based on
what information that is available. When statistics of failures are reported, it is inconvenient
to have reports sheets with every component down to the smallest bolt, instead they are
grouped according to a set of components based on their function. Failure reports from
Germany, Sweden and Finland are divided into the same set of components and they are
basically based on their function within the system.
2.3 The components of the wind power system
The names of the components are general and apply to almost all designs of wind turbines.
The terminology used for the components comply with the same terminology used within the
wind power industry. The system components described here are for a common system with
the basic features.
Rotor blades
Wind
Hub
Nacelle
Wind
Tower
Foundation
Figure 1: Overview of different parts of a Wind Power Plant
2.3.1
Rotor blades and Pitch system
The wind makes the rotor blades turn, thus making the shaft inside the wind turbine turn.
There are different designs of the blades but lightweight and sturdy are the basic features. The
blades are generally made from glass fiber reinforced plastic. The reinforcement can also be
carbon fiber or laminated wood. Some blades have advanced techniques for lightning
protection built into the blade. Another feature of some blades, is heating inside the blades to
be used in arctic climates.
6
Reliability performance and maintenance – a survey of failures in wind power systems
The most common design is a three-bladed rotor. The two-bladed rotors are used
commercially but most manufactures prefer to produce three bladed rotors. A two bladed
rotor spins faster than a rotor with three blades and might appear less appealing to the eye [4].
Closely interconnected to the rotor blades is the pitch system. The objective of the pitch
system is to regulate output power at high operational wind speeds. This involves turning the
blades about their long axis (pitching the blades) to regulate the power extracted from the
rotor. Pitch regulation changes the rotor geometry and this involves active control of the
system to sense blade position, measure output power and to instruct changes of the blade
pitch [4]. The pitching angle is controlled by the control system and is usually regulated by a
hydraulic system but electrical motors for pitching the blades are also available. Not all wind
turbines use the pitching technique; some rely on other techniques to regulate the power
output.
Pitch regulation also makes it possible to smoother start up the wind power turbine as wind
increases. Since pitching offers a better output, these are favored among larger turbines. The
thrust of the rotor on the tower and foundation is lower for pitch-regulated turbines and this
allows for reduction of material and weight.
2.3.2
Hub
The hub is seldom separately defined in failure statistics but is categorized as a part of the
structure. For the complete understanding of the structure of the wind power plant the hub is
shown separately in Figure 1. The hub is the centered construction, which connects the blades
to the main shaft. The hub is usually made out of cast iron [5]. Inside the hub is electrical and
mechanical equipment for controlling the blades.
2.3.3
Structure – Tower, Foundation and Nacelle
The structure consists of the tower, and the nacelle and the rotor that it carries. Generally, it’s
better to have a high tower, as wind speeds increase further away from the ground. When
examining failure statistics one finds that the component ‘structure’ usually includes the
foundation beneath the tower and the nacelle. The nacelle is the housing for the gears and the
electric generator at the top of the tower, see Figure 1 and also Figure 2.
7
Reliability performance and maintenance – a survey of failures in wind power systems
Electrical
system
Drive train
Gearbox
Generator
Figure 2: View inside the nacelle
2.3.4
Drive train
The drive train basically consists of the shaft and the bearings and occasionally a clutch
between the gearbox and the generator. In Figure 2 the drive train is represented by a single
‘box’, but in reality it is the interconnecting shafts between the hub, the gearbox and the
generator. The shaft goes into the nacelle from the hub, where the blades are connected, and
connects to the gearbox. The shaft rotates with low speed and needs to be geared up, which is
done in the gearbox. On the other side of the gearbox the high-speed shaft exits into the
generator.
When examining the different components within the wind power system one finds many sets
of bearings at different locations where there are rotating machinery. The bearing that is
mentioned as a part of the drive train is present if the turbine is constructed with a main
bearing. Another way of designing the turbine is by implementing the main bearing directly
into the gearbox.
2.3.5
Gearbox
The gearbox transforms low-speed revolutions from the rotor to high-speed revolutions. To
transform the low rotational speed of about 30 rpm to 1500 rpm, usually three stages are
needed. The design of the gearbox is subject to constant changes. At the moment a common
solution is to use a planetary stage gear which has a feature of being very compact. Via a
high-speed shaft the gearbox is then connected to an electric generator. A high speed
revolution of about 1500 rpm is a requirement for transforming rotational energy to electrical
power of good frequency. Less rotational speed is needed if the generator has more pole pairs.
2.3.6
Generator
The type of generator used in the wind turbine varies, but usually it is an induction generator
or a double fed induction generator, DFIG. The generator transforms the rotational energy
into electrical energy. The generator is connected to the electrical system and supplies the
transformed energy to the electrical system.
8
Reliability performance and maintenance – a survey of failures in wind power systems
2.3.7
Electrical system
This is basically all equipment required to deliver and control the electrical energy that
follows from the generator to the grid. The electrical energy usually has to be controlled in
different ways depending on amount of active and reactive power, voltage and phase. Modern
designs let the power output from the generator pass through a set of power electronic
components to control the power and the frequency before supplying it to the grid. The
boundary for the wind power system in this thesis work is between the electrical system and
the grid.
2.3.8
Control system
The control system is made out of a main computer inside the nacelle or in the tower
structure. The control unit surveys the power output, wind and wind direction and controls the
settings so that the pitch and the yaw can be optimized. The control system is connected to
several sensors within the wind power structure. This control system is not to be confused
with condition monitoring systems. The function of the control system is only to supervise the
system so that performance at the moment is optimized, the safety of the system is maintained
and alarms are reported in case some sensor signal is above a set parameter limit value. In
larger wind farms the control systems from different turbines are monitored by a centre of
operations.
2.3.9
Sensors
In a typical turbine there are about 30 to 50 monitoring sensors; more modern turbines have
more sensors, about 2000. These sensors include wind measurement equipment as well as
sensors for temperature, wind direction, vibrations, revolutions, cable twist etc. The sensors
are connected to the control system. If a CMS is installed in a turbine, some sensors can be
shared and some need to be independent. For the most basic condition monitoring, i.e.
vibration monitoring of the gearbox, only about eight measuring points are needed but
modern CMS integrate measuring values from other parts of the system just like the control
system, e.g. temperature, wind direction etc, hence more measuring points are needed.
2.3.10 Mechanical brakes
Mechanical brakes are essential for safety reasons. During high winds and repair it is crucial
that these brakes are functional. The wind power system can utilize both aerodynamic brakes
and mechanical brakes. Aerodynamic brakes are when the blades are pitched into a position
where as less wind force as possible is absorbed. The mechanical brake system consists of a
disc break in conjunction with the gearbox.
2.3.11 Hydraulic system
Hydraulic components are used in the turbine. Pitching, braking and yawing are features
within the turbine that commonly rely on hydraulic systems.
9
Reliability performance and maintenance – a survey of failures in wind power systems
2.3.12 Yaw system
The yaw system is the system for controlling how the tower turns, because as the wind turns
the nacelle needs to adjust itself so it faces the wind properly. This system contains bearings,
gearwheels, brakes and a yaw motor.
10
Reliability performance and maintenance – a survey of failures in wind power systems
3 Reliability theory
“The main objective of a reliability study should always be to provide information as
a basis for decision.” - Rausand and Hoyland, 2004 [6]
The results provided by a reliability study will not tell us exactly what to do, but in what
direction to look. For example, a reliability study can be useful in areas of risk analysis,
optimization of operations and maintenance. The risk analysis is a way of identifying causes
and consequences of failure events, and the optimization is a way of telling how failures can
be prevented and how to improve the availability of a system. One can see reliability theory
as a tool for analysing and improving the availability of the system.
3.1 Definitions
A definition of reliability is: the ability of an item to perform its required function under given
conditions for a given time interval [10]. This ability can be described in terms of probability
and the probability distribution may be used to model the lifetime of a component.
Examples of probability distributions is shown in chapter 3.2 and examples of measurements
of reliability and availability is shown in chapter 3.4
3.2 Probability distributions and their applications
To model the lifetime of components probability distributions may be used. There are several
different types of distributions suitable for different kind of applications. In this thesis only
the Weibull and the exponential distribution will be considered.
3.2.1
The Weibull distribution and the exponential distribution
The Weibull distribution is a widely used life distribution in reliability analysis. The
distribution is very flexible and can through an appropriate choice of parameters model many
types of failure rate behaviours. [6] The Bathtub-curve can be modelled easily with three
different sets of parameters respectively for the three different phases. The distribution for the
useful life period is a special case of the Weibull distribution. This special case of Weibull
distribution is equal to an exponential distribution. Hence for the useful life period, the
exponential distribution is used.
Equation 1: The Weibull distribution
f (t ) = λβ βt β −1e − ( λt )
β
for t > 0
Equation 2: The exponential distribution
f (t ) = λe − λt
for t > 0
The use of exponential distribution for lifetimes comes with a number of important side
effects.
11
Reliability performance and maintenance – a survey of failures in wind power systems
•
•
•
3.2.2
The failure rate is constant which means that it is independent of time. [6]
The exponential distribution has no memory, so an item is always viewed as good as
new as long as it is functioning. [6]
When estimating the reliability function, the mean time to failure and so on, it is
sufficient to collect data on the number of hours of observed time operation and the
number of failures. The age of the component is of no interest in this context. [6]
Bathtub curve and other shapes of curves
“Normal mechanical failure modes degrade at a speed directly proportional to their
severity. Thus, if the problem is detected early, major repairs can be prevented in
most instances.” - Davies, 1998 [7]
According to Davies [7] one needs to find the right time for the failure to prevent major
repairs, but before trying to find the time for a failure one needs to examine and learn more
about the lifetime of the component.
The failure rate of a component is often high in the initial phase of its lifetime. This can be
explained by the fact that there may be undiscovered defects in the components [6]. When the
component has survived the initial period, the failure rate stabilizes at a level where it remains
for a certain time until it starts to increase again as the component begin to wear out. The
shape of the curve depicting the failure rate of the component, is similar to that of a bathtub,
hence the expression bathtub-curve. Figure 3 shows the bathtub curve with the three typical
phases. The initial phase is called burn in period, the stable phase is called useful life period
and the end phase is called wear out period. Other examples of names for these three periods
are break in, operations and breakdown. This terminology varies in literature but the main
concept of three different stages in the life of the component or system are still the same.
Number of
failures
Burn-in
period
Useful life
period
Wear out
period
time
Figure 3: The Bathtub curve
Figure 3 gives one example of a possible shape for the failure function. There are other failure
functions with other shapes, but the bathtub curve appears as a good choice for mechanical
components such as gearboxes, which later on will be studied further (see chapter 6). For the
12
Reliability performance and maintenance – a survey of failures in wind power systems
majority of mechanical items the failure rate function will usually show a slightly increasing
tendency during the useful life period, because of the wear on the mechanical components [6].
3.3 The Alternating Renewal Process
When a component fails, immediate repair is undertaken and when the repair is done, the
component is put back into the system and is considered as good as new, hence the expression
renewal.
status of system
Failure occurs
”as good as new”
1
0
Wear time
Repair time
time
Figure 4: Alternating Renewal process
3.3.1
Wear model
To be able to understand and to apply theoretical tools to a physical component models are
used. One way of modelling the system is by setting it to one of two states: up or down,
failure or no failure, see also Figure 4. We can picture the state of the system as a binary
process. The statistical data used in this thesis is only based on the stages; up or down, hence
only a model with two states will be used
3.3.2
Improved wear model
It is also possible to look at models with intermediate states between completely new and
completely failed. In this type of model, failure is a damage accumulation process [8], see
Figure 5. A good example is mechanical deterioration, where there are several states between
brand new and failed.
“Wear is defined as the progressive loss of substance resulting from mechanical
interaction between two contacting surfaces.” - Davies, 1998 [7]
A model with several states appears suitable for systems with monitoring equipment. The
wear model with different stages of deterioration is applicable when analysing specific
components where the different stages of wear have been well defined.
13
Reliability performance and maintenance – a survey of failures in wind power systems
status of system
Damage accumulating
process
”as good as new”
1
0
wear time
repair time
time
Figure 5: Damage accumulating process
Note: For this thesis work, no information of intermediate stages of wear is available thus the
process used in the thesis is an alternating renewal process, described in Chapter 3.3.
3.3.3
Repair time
The repair time can be modelled similarly to the lifetime of operations. There is a suitable
distribution for repair time, the lognormal distribution, which for example takes into account
that some repairs can be made quickly while other repairs rely on spare parts that are not
available at the moment. It is also common to use the exponential distribution for repair time.
The repair time is of course important when detailed models of the maintenance are
considered but as we will later find out it is difficult to find data concerning repair of wind
power turbines and yet more difficult to find out the exact amount of time spent on repair. The
information that may be available is the amount of time that the system was unavailable, but
this time may consist of scheduled maintenance and stoppages caused by other events not
connected to any failure. In this thesis a model of exponential distribution for repair time will
be considered.
3.4 Measurements
To be able to acquire useful information about the performance of a system or component,
some measurements of the reliability and availability have to be used. Later in the analysis of
data form the wind power plants these measurements will be used in order to compare
different components and different systems
3.4.1
Measurements of reliability performance
The reliability can be measured in many ways depending on the particular situation, for
example as: Mean time to failure or number of failures per time unit or failure rate. [6]
The mean time to failure, MTTF, is defined as the mean time between initial operation and
the first occurrence of a failure or malfunction, as the number of measurements of such time
on many pieces of identical equipment approaches infinity. When a failure has occurred the
item is repaired and put back into operation and the item is then considered as fully
functioning.
The mean down time, MDT, is defined as the average time that the system is not functioning
when a component is being repaired, and is basically the time it takes to repair a failure. The
14
Reliability performance and maintenance – a survey of failures in wind power systems
mean time between failures, MTBF, takes into account the mean time to failure and the mean
down time. The down time is usually much shorter than the time of operations and then the
two measurements can be viewed as: MTTF ≈ MTBF, see Figure 6.
Operating
status
Up
Down
MTTF
MDT
time
MTBF
Figure 6: Measurements of reliability
3.4.2
Measurements of availability performance
The availability performance is defined as:
“the ability of an item to be in a state to perform a required function under given
conditions at a given instant of time or during a given time interval, assuming that the
required external resources are provided” – Maintenance terminology, SIS 2001 [10]
By using the measurements of reliability performance, i.e. MTBF and MTTF, the availability
for the system can be described as the portion of operational time, MTTF, over a nominal
period of time, in this case MTBF, given that the time t approaches infinity. In Equation 3 the
equation for such a measurement of availability is shown.
Equation 3: Availability [6]
Availability =
MTTF
MTTF
=
MTTF + MDT MTBF
when t → ∞
The measurement of availability differs within wind power. A commonly used measurement
of availability is the amount of operational time divided by the nominal time, see Equation 4.
The nominal time is usually a period of one year and then the availability is presented as
percentage of operational time per year. This type of definition is used in Sweden and within
the WMEP research in Germany.
Equation 4: Availability [9]
Availablity =
Nom.time − Downtime
Nom.time
Another way of expressing the availability is to eliminate downtimes not caused by the wind
power plant, such as external failures of the grid, see Equation 5. This type of definition is
used in Finland.
15
Reliability performance and maintenance – a survey of failures in wind power systems
Equation 5: Availability with regard to grid disturbances [16]
Availablity =
Nom.time − ( Downtime − Downtime caused by gridfailures)
Nom.time
Note: These two different definitions of the availability have been used in the sources for the
statistical data. The effect these differences have on the result is not investigated, but
assumptions say that the two different definitions will not influence the result.
A third option to use for availability is to not use the nominal time of one year but the actual
available operational time. E.g. The available operational time is only when the wind is
blowing and not when the plant has stopped due to low winds or to high winds.
Unavailability is the period which the plant is not functioning. This can be scheduled
downtime (maintenance) or unscheduled downtime (malfunction or failure).
16
Reliability performance and maintenance – a survey of failures in wind power systems
4 Maintenance methods
Maintenance is required for almost all types of machinery and applies also to the wind power
system. The type of maintenance that is performed can be defined as either preventive or
corrective maintenance. Preventive maintenance is carried out at predetermined intervals or
according to prescribed criteria and is intended to reduce the probability of a failure.
Corrective maintenance is carried out after a failure and is intended to repair the system. [10]
In other words, preventive maintenance is performed before a failure and the corrective is
preformed after the failure occurs.
“An ideal maintenance strategy meets the requirements of machine availability and
operational safety, at minimum cost.” - Rao, 1996 [11]
Consequently the challenge in planning the maintenance is to decide on when to perform
preventive maintenance.
In this chapter an explanation of three different methods for maintenance is presented;
corrective maintenance and two types of preventive maintenance; scheduled maintenance and
condition based maintenance, see Figure 7.
Maintenance
Preventive
Maintenance
Condition based
Maintenance
Corrective
Maintenance
Scheduled
Maintenance
Figure 7: Classification of maintenance types [6]
17
Reliability performance and maintenance – a survey of failures in wind power systems
4.1 Corrective maintenance
Corrective maintenance is defined as [10]:
Corrective maintenance - Maintenance carried out after fault recognition and
intended to put an item into a state in which it can perform a required function.
This type of maintenance is often called repair and is carried out after the failure of a
component. The purpose of the corrective maintenance is to bring the component back in to a
functioning state as soon as possible, either by repairing or replacing the failed component.
[8]
To only use corrective maintenance is seldom a good solution. This means that you will run
you system until a breakdown occurs and in some literature this is referred to as a breakdown
strategy. [23]
With a breakdown strategy the preventive maintenance is reduced to a minimum and the
system will be operated until a major failure of a component occurs which will result in a
shutdown of the wind turbine. This strategy is risky, since failures of relative small and
dispensable components can lead to severe consequential damages. Another aspect of such a
strategy is that most component failures are likely to be related to the actual load condition of
the wind turbine and is also likely to happen during high load conditions. This means that the
shutdown of the turbine is related to high wind periods. Downtime in such periods will lead to
higher production loss. If the wind turbine is situated offshore, the accessibility is likely to be
bad during high wind periods. [23]
Another drawback of this strategy is that when repair is needed the downtime can be
extensive since logistics gets more complicated and delivery periods for spare parts can be
long. A breakdown strategy minimizes the cost for repair and maintenance during operation.
With no knowledge of the consequence of a failure until it occurs makes it impossible to
calculate the costs of replacements. The lifetime of the component is unpredictable and only
once the component has failed can an assessment of the cost and lifetime be made. [11]
Condition [%]
Scheduled
maintenance
Corrective maintenance
Breakdown
Time
Figure 8: Corrective Maintenance compared to Scheduled Preventive Maintenance
18
Reliability performance and maintenance – a survey of failures in wind power systems
4.2 Preventive maintenance
Preventive maintenance is defined as [10]:
Preventive maintenance – Maintenance is carried out at predetermined intervals or
according to prescribed criteria and intended to reduce the probability of failure or
the degradation of functioning of an item.
The preventive maintenance is performed regularly to postpone failures or to prevent failures
from occurring. There are two different types of preventive maintenance; the scheduled
maintenance and the condition based maintenance. What differs between these two are the
way of deciding when to perform the preventive maintenance.
4.2.1
Scheduled maintenance
Scheduled maintenance is defined as [10]:
Scheduled maintenance - Preventive maintenance carried out in accordance with an
established time schedule or established number of units of use.
Scheduled maintenance means that preventive maintenance is carried out in accordance with
an established time schedule [10]. The time-schedule for the preventive maintenance can be
either clock-based or age-based maintenance. Clock-based maintenance means that the
preventive maintenance is carried out at specified calendar times and age-based maintenance
means that the maintenance is carried out when a component reach a certain age. The age
does not need to be calendar time, but measured in for example revolutions or operational
time etc. [6]
Preventive maintenance performed at scheduled intervals should be designed to reduce the
probability of failures. Maintenance cycle times will be matched to the requirements of the
system. The system will be inspected and maintained periodically, see Figure 8. The
components that first show sign of wear and fatigue will be maintained and replaced. This
type of maintenance strategy means that components exposed to wear will be replaced
regularly even if they are not at the end of their lifetime.
Scheduled maintenance requires regular access to the system and a big share of the costs for
the maintenance will stem from the supply for cranes and maintenance personnel. Transport
of personnel and spare parts to the wind farm can also be cost intensive with this preventive
maintenance strategy. The advantage of preventive maintenance is that it can be scheduled
ahead of time and the coordination of logistics can be made easy. [23]
19
Reliability performance and maintenance – a survey of failures in wind power systems
4.2.2
Condition based maintenance
Condition based maintenance is defined as [10]:
Condition based maintenance – Preventive maintenance based on performance
and/or parameter monitoring and the subsequent actions. Performance and
parameter monitoring may be scheduled on request or continuous.
Condition based maintenance is a type of preventive maintenance that is based on the
performance and monitoring of parameters from the system. With this type of preventive
maintenance, monitoring equipment collects machine data. The condition monitoring may be
scheduled, on request or continuous. The collected machine data can indicate required
maintenance prior to predicted failure. Maintenance is initiated when a condition variable
approaches or passes a threshold value. The system components will be operated to a defined
condition of wear and fatigue. When this condition is reached, the component needs be
maintained or replaced. [23] Examples of condition variables that the system monitors are
vibration, temperature, number of particles in the lube oil etc.
The ability to monitor the condition of components facilitates planning of maintenance prior
to failure and will minimize downtime and repair costs. The components will be used closer
to their lifetimes and the coordination of spare parts will be easy. Another benefit of
implementing a condition based system is that trends and statistical data such as mean time to
failure can be provided.[11] The statistical data from monitoring system is important for
getting reliable data for remaining lifetime of components in the system. With site specific
data the prediction of remaining time for the components can be more precise.
Figure 9 shows an example of condition based maintenance along with corrective and
scheduled maintenance.
Condition [%]
Scheduled
maintenance
Condition based
maintenance
Corrective maintenance
Breakdown
Time
Figure 9: Condition based maintenance compared to scheduled and corrective maintenance
20
Reliability performance and maintenance – a survey of failures in wind power systems
4.3 Comparison of maintenance methods
Figure 9 shows a graphical example of possible scenarios for maintenance. The comparison
shows that scheduled maintenance is performed more often than condition based
maintenance. The example also shows that the lifetime of the component is not fully used in
the scheduled maintenance compared to the use of corrective- or condition based
maintenance.
Table 1 shows some advantages and disadvantages found for the different maintenance
methods when applied to wind power.
Table 1: Comparison of maintenance methods.
Method
Corrective
Maintenance
Advantage
• Low maintenance costs during
operation.
• Components will be used for a
maximum lifetime.
Preventive
Maintenance Scheduled
•
•
•
Expected downtime is low
Maintenance can be scheduled.
Spare logistics is easy
Preventive
•
Maintenance Condition based •
•
Components will be used up to
almost their full lifetimes.
Expected downtime is low.
Maintenance activities can be
scheduled.
Spare part logistics is easy
given that a failure can be
detected early in time.
•
Disadvantage
• High risk in consequential
damages resulting in extensive
downtimes.
• No maintenance scheduling is
possible.
• Spare parts logistics is
complicated.
• Long delivery periods for parts
are likely.
• Components will not be used for
maximum lifetime.
• Maintenance costs are higher
compared to corrective
maintenance.
•
•
•
•
•
Reliable information about the
remaining lifetime of the
components is required.
High effort for condition
monitoring hardware and
software is required.
Cost of another layer in the
system.
Not a mature market for
monitoring systems within wind
power.
Identification of appropriate
condition threshold-values is
difficult.
Sources: [7], [3], [23]
21
Reliability performance and maintenance – a survey of failures in wind power systems
4.4 Maintenance strategy
With the three methods presented a maintenance strategy can be implemented. The strategy
will be a combination of preventive and corrective maintenance. The use of condition
monitoring equipment makes the condition based maintenance a good option as to reduce cost
related to maintenance. Logistics can be planned ahead and the lifetime of the components
can be almost completely utilized.
“A condition monitoring programme can minimize unscheduled breakdowns of all
mechanical equipment in the plant, and ensure that repaired equipment is in an
acceptable mechanical condition. The programme can also identify machine train
problems before they become serious. “ - Davies, 1998 [7]
The use of condition based maintenance is relatively new within wind power and more issues
of concern on condition monitoring systems will be presented in chapter 7.
22
Reliability performance and maintenance – a survey of failures in wind power systems
Analysis
5 Survey of failures for wind power turbines
The study of statistical data from wind power turbines is important since it gives knowledge
about the reliability performance. Operational data will verify if the predicted lifetime of the
system is correct and experience gained from the analysis of statistical data may result in a
redesign of a component or even a change in the maintenance planning.
5.1 Access to statistical data
In the pre-study of this thesis it became evident that statistical data of failures was difficult to
find. This was for several reasons, such as: no data was collected; the manufacturers of wind
turbines refused to reveal data; the data are often not comparable due to different designs;
some data are so expensive to access that it is simply not worth the effort, etc.
These constrictions led to the following decision concerning data for the thesis:
• Data needs to be comparable
• Data needs to be within the same or similar time span
• Data needs to be reliable
The statistical data used for this thesis work comes from four different sources, which obey
these restrictions and also stems from the same region, northern Europe.
5.1.1
Sources for statistical data
In this thesis work four sources for data are used. Sources 1 and 2 stem from Sweden and
partly overlap each other. A comparison of the sources can be found in Table 2:
1. Driftuppföljning av vindkraftverk, årsrapport (Wind power operations, yearly report),
Sweden – A early published report from Elforsk, which concerns statistical data of
performance, failures and downtimes for almost all wind power plants situated in Sweden.
2. Felanalys (Failure analysis), Sweden – A database of all reported failures in Sweden
since 1989 maintained by Swedpower AB. The database was created in 1997 but also
contains failures that occurred prior to the starting date as far back as 1989. This database
is also a part of the source for Driftuppföljning av vindkraft, årsrapport but it contains
more information about the failures compared to what is published in the yearly report.
3. Tuulivoiman Tuotantotilastot Vuosiraportti (Wind power production statistics yearly
report), Finland – A yearly published report from VTT, which concerns statistical data of
performance, failures and downtimes for wind power plants situated in Finland.
4. Windenergie Report Deutschland (Wind energy report Germany), Germany – A yearly
published report from Institut für Solare Energieversorgungstechnik (ISET), which
concerns statistical data of performance, failures and downtimes for wind power plants
situated in Germany.
23
Reliability performance and maintenance – a survey of failures in wind power systems
Table 2: Statistical sources used in this thesis
Name of survey Driftuppföljning
av vindkraftverk,
årsrapport [9]
Felanalys [12]
Tuulivoiman
Tuotantotilastot
Vuosiraportti
[16]
Finland
2000-2004
Windenergie
Report
Deutschland
[26]
Germany
2003-2004
Sweden
Sweden
Country
1997-2004
1989- oct 2005
Time span of
data in survey
723*
786*
92
650
Number of
turbines in
survey (2005)
>95%
>95%
~100%
4% - 7%
The data
coverage of all
turbines in
country
1658
1658
491
4807
Accumulated
number of
reported
failures
*
Note: The difference between the two sources is because during this time span 36 turbines
have been dismantled or transferred to the Finnish statistics and 27 turbines were erected
during 2005.
5.1.2
Comparable data
It is difficult to judge if the data from the four sources are comparable since the background
source material is not available. The assumption that these reports are similar is based on the
observations that the report sheets used for failures are almost identical and the terminology
used within these four sources is comparable. Appendix 1 presents a report sheet used for
reporting failures of Swedish wind power turbines.
Thirdly, another reason for assuming that these data are comparable is that the basic
construction of the wind power plant is similar regardless of brand. Fourthly, the region of
northern Europe where the sources for the statistical data is taken from is similar and site
conditions such as weather does not affect the data from one country in any way more than
the others.
Finally the fifth reason for treating the data as comparable; is that all major manufacturers of
wind power turbines are represented in all of the sources.
5.1.3
Time span of data
Similar time spans have been used to make a comparison between the three different
countries. For the four different sources, only these time spans have been available for
statistical data:
• Sweden, published reports for the years 1997-2004
• Sweden, failure database with reports for the years 1989-2004
• Finland, published reports for the years 2000-2004
• Germany, published reports for the years 2003-2004
24
Reliability performance and maintenance – a survey of failures in wind power systems
For most of the following analysis trends for these years have been used. In cases where
additional data have been found or in figures where data is presented, the time span will be
noted.
5.1.4
Reliability of data
The data from the three countries are gathered mostly on a voluntary basis. In earlier years
some wind power plants got funding from the government or through research projects and
one of the requirements for the funding was that statistical data had to be collected. In recent
years some of this funding has stopped but the measuring of statistical data continues.
The failure reports that are handed in to the statistical gathering authorities whenever a failure
occurs vary in quality. In some cases a professional report is filed. In other cases a layman
may fill in the report and then there is a chance of misinterpretation of what actually failed.
It is not possible to draw any detailed conclusion solely out of these data, but trends and major
conclusions can (and will later on) be shown. The findings are analyzed further and confirmed
with the aid of interviews with individuals holding expertise in the area of wind power.
5.2 Failure statistics
Operational statistics from wind power plants are regularly collected by the control unit inside
the turbine. Today, most turbines are fitted with equipment that makes it possible to collect
the data remotely, via modem or internet.
The data from the sources that was accessible for this thesis are assembled by governmental
authorities or research groups. Participants in special research programmes or companies that
get subsidies are obligated to report not only performance but also the downtime and the
failures of the turbines to the authorities or research groups in their respective country.
5.2.1
General procedure to restore a failure
When a failure occurs, a typical procedure for handling the failure may look like this [28],
[29];
1. A failure occurs inside the wind turbine, e.g. the gearbox fails.
2. The control unit inside the turbine registers either the failure directly or the consequence
of the failure and acts according to what type of failure has occurred. In case of safety
hazard or major damage, the turbine is shut down.
3. If the unit is remotely monitored an alarm is sent to the operators of the wind power plant.
4. Many alarms are not crucial and often the wind turbine can be restarted again. If the
failure of the turbine is of a severe kind, a visual inspection of the turbine has to be made.
This inspection can be performed by the operators or by locally authorized personnel.
5. If a major failure has occurred, maintenance and repair personnel have to be contacted to
repair the damage or replace the damaged parts. When a major failure has occurred a
report is filed describing which parts that was involved and possible causes and the
downtime related to the failure.
6. The report is maintained and transformed into databases by the persons responsible for
gathering the statistical data.
25
Reliability performance and maintenance – a survey of failures in wind power systems
5.2.2
The failure report sheet
The failure reports are designed with check boxes and uses similar terminology in the three
investigated countries, Sweden, Finland and Germany. Appendix 1 shows an example of the
Swedish report sheet.
5.3 Statistics from Sweden
The statistical data from the wind power systems in Sweden is collected by Swedpower AB
which in turn performs this task on behalf of Elforsk. Every year Elforsk publishes a report on
the performance of the turbines in Sweden including values of downtimes for different
component failures [9].
Almost all Swedish wind turbines are connected to a system with automatic readings of
turbine performance. Failure reports are not automated but are handed in as reports and are
then compiled in a database. This database is handled by Swedpower AB. The database
contains information about the production, downtimes as well as failure reports that dates
back to 1989.
The following figures present an overview of failures and downtimes for the period 20002004. The amount of installed turbines during this period has changed over the years as more
turbines are installed, see Table 3.
Table 3: Total amount of installed turbines used in survey in Sweden.
Year
2000
2001
2002
2003
2004
Number of turbines in survey
723
26
527
570
620
682
Average during
2000-2004
624,5
Reliability performance and maintenance – a survey of failures in wind power systems
5.3.1
Failure frequency
Figure 10 shows the percentage breakdown of the number of failures that occurred during the
years 2000-2004. Most failures were linked to the electrical system followed by sensors and
blades/pitch components. A full overview of the values for frequency of failures is shown in
Table 4.
Distribution of Number of failures [%]
Entire unit 2,7
Structure 1,5
Hub 0,3
Blades/Pitch 13,4
Yaw System 6,7
Generator 5,5
Hydraulics 13,3
Mechanical Brakes 1,2
Electric System 17,5
Gears 9,8
Sensors 14,1
Control System 12,9
Drive train 1,1
Figure 10: Distribution of number of failures for Swedish wind power plants between 2000-2004
Source: Felanalys, Database Swedpower AB 2005 [12]
27
Reliability performance and maintenance – a survey of failures in wind power systems
5.3.2
Downtime
Figure 11 shows the distribution of downtime per component in Sweden between the years
2000 and 2004. Data for this figure is taken from the yearly published report from Elforsk.
The most troublesome component is the gearboxes closely followed by the control system as
well as the electric system. This means that the gearbox has the longest downtime compared
to the other components. A full overview of the values for frequency of failures is shown in
Table 4.
Distribution of Downtime [%]
Entire unit 1,7
Hub 0,0
Structure 1,2
Yaw System 13,3
Blades/Pitch 9,4
Generator 8,9
Hydraulics 4,4
Mechanical Brakes 1,2
Electric System 14,3
Gears 19,4
Control System 18,3
Sensors 5,4
Drive train 2,4
Figure 11: Percentage of downtime per component in Sweden between 2000-2004
Source: Driftuppföljning av Vindkraftverk Årsrapport 2000-2004 [9]
Table 4 also shows annual frequencies of failure and downtimes for a turbine. These
calculations have taken into account that some turbines neither use hydraulics nor gearboxes.
A good measurement of the severity of a failure is to consider how often a component fails
and for how long the problem lasts, which is the average downtime per failure. If for example
a component fails often but has a very short downtime then this does not disturb the
production noticeably compared to a failure that seldom occurs but has long downtimes.
The most important findings from the values in this table are that a typical turbine in Sweden
has a failure 0,402 times a year and the mean downtime for each failure is 130 hours a year.
The most critical components are the drive train, the gearbox and the yaw system which take
250 to 290 hours to repair
28
Reliability performance and maintenance – a survey of failures in wind power systems
Table 4: Downtimes and failure frequencies for Swedish wind power plants 2000-2004
Distribution
average
Average
Average
Average
Component
Total
Total number
of
downtime per
downtime
downtime
of failures per number of number of
downtime,
year per
failures per
per year
failures
per
component,
2000 -2004
turbine
year per
[h/yr]
component,
2000-2004 [n] per year
[%]
[h/yr/turbine]
turbine
[n/yr]
2000-2004 [h]
[n/yr/turbine]
(Figure 11)
Hub
50
10
0,0
0,0
4
0,8
0,001
Blades/Pitch
14743
2949
4,7
9,4
161
32,2
0,052
Generator
13906
2781
4,5
8,9
66
13,2
0,021
Electric
system
22395
4479
7,2
14,3
210
42,0
0,067
Control
system
28620
5724
9,2
18,3
155
31,0
0,050
Drive train
3788
758
1,2
2,4
13
2,6
0,004
Sensors
8357
1671
2,7
5,4
169
33,8
0,054
Gears
30286
6057
11,6
19,4
118
23,6
0,045
Mechanical
brakes
1881
376
0,6
1,2
15
3,0
0,005
Hydraulics
6918
1384
2,6
4,4
160
32,0
0,061
Yaw system
20754
4151
6,6
13,3
80
16,0
0,026
Structure
1874
375
0,6
1,2
18
3,6
0,006
Entire unit
2631
526
0,8
1,7
33
6,6
0,011
Total
156202
31240
52,4
100,0
1202
240,4
0,402
Source: Driftuppföljning av Vindkraftverk Årsrapport, 2000-2004 [9] and Felanalys - Database Swedpower AB 2005[12]
Distribution
of failures,
2000 -2004
[%]
(Figure 10)
Average
downtime per
failure, 20002004 [h/failure]
0,3
13,4
5,5
12,5
91,6
210,7
17,5
106,6
12,9
1,1
14,1
9,8
184,6
291,4
49,4
256,7
1,2
13,3
6,7
1,5
2,7
100,0
125,4
43,2
259,4
104,1
79,7
130,0
29
Reliability performance and maintenance – a survey of failures in wind power systems
5.3.3
Failures versus operational year
The previous survey used only covered failures for the most recent five year period. Another
way of examining the data is by investigate the relationship between the amount of failures
and the age of the turbine, i.e. operational year.
In Figure 12 the total number of failures per operational year is shown together with the
number of turbines that have “survived” respective operational year. Failures for turbines with
up to 19 years of operation have been recorded.
350
800
300
700
600
250
500
200
400
150
300
100
200
50
Number of turbines
Number of failures
Number of failures
100
0
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19
Operational year
Number of failures
Number of turbines
Figure 12: Number of failures vs. operational age.
Source: [12]
Since so many turbines have been installed recently, that is within the last 8 years, a
recalculation of failure frequency is needed. In Figure 13, the annual failure rate per turbine
versus operational year is plotted. This figure gives a better view of the development of the
failures since it takes into account the population, i.e. the number of turbines within every
operational year.
30
Reliability performance and maintenance – a survey of failures in wind power systems
Failure rate
0,8
0,7
Annual failure rate
0,6
0,5
0,4
0,3
0,2
0,1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Operational year
Figure 13: Annual failure rate per turbine vs. operational year
Source: [12]
The number of failures in the first operational year is much lower than the second year. A
reason for this could be that not all failures are reported during the run in period of a new
turbine. The following years the failure rate is constant until about five years of operation
where the failure rate seems to drop.
In year 12 there is a peak and in the following years towards year 19 there seems to be an
upward trend, but for these last two findings one must also consider the small amount of data
and the diminishing population of turbines. The failures for year 18 and 19 constitutes of the
failures from only a few surviving turbines.
A breakdown of the failures and the failure rate into groups of rated power of <500kW,
500kW-1000kW and >1000kW gives a more detailed overview of how the failures distributes
among the power groups, see Figure 14 and Figure 15.
31
Reliability performance and maintenance – a survey of failures in wind power systems
Amount of failures per operational year
180
160
Number of failures
140
120
100
80
60
40
20
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
operational year
Rated power <500
Rated power 500<1000
Rated power >1000
Figure 14: Number of failures in respective rated power group versus operational year
Source: [12]
Frequency of "failure rate" with increasing operational age
2,5
Annual failure rate
2
1,5
1
0,5
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
operational year
Rated power <500
Rated power 500<1000
Rated power >1000
Figure 15: Failure rate in respective rated power group versus operational year
Source: [12]
In Figure 15 the trends of different power groups is shown and turbines rated below 500kW
and between 500kW and 1000kW follows the same pattern of a slight increase in failures
during the first three years of operation and then after five years the annual failure rate
decreases. The most remarkable finding is that the turbines rated above 1000kW have a
higher annual failure rate compared to smaller turbines. The second important finding is that
the trend of decreasing or constant failure rate is not found for the turbines rated above
1000kW. Instead it appears as the failure rate is increasing.
32
Reliability performance and maintenance – a survey of failures in wind power systems
5.3.4
Gearboxes – a deeper study
Previous findings have shown that the gearbox is a troublesome component within the wind
power turbine. For a typical turbine, 20 percent of the downtime is due to gearbox failures and
an average gearbox failure takes about 256 hours to repair. Since the gearbox is so critical for
the availability of the turbine, some more statistics concerning gearboxes in Swedish power
plants is presented.
In Table 5 failures for gearboxes between 1997 and 2004 is shown. This table includes all
gearbox failures that have occurred since the start of the database. In some cases the failure
statistics is reported and put into the database much later than the report is printed hence there
might be some small deviance between the officially published report from Elforsk and the
figures found in the database at Swedpower AB. These small differences can be found in the
amount of hours in reported downtime but this does not affect the overall conclusion that
gearboxes constitute a great part of the total downtime.
Table 5: Overview of data for gearbox failures in Sweden between 1997 and 2004
Year
1997 1998 1999 2000 2001
2002 2003
2004
19972004
232
41895
181
Number of failures [n]
21
41
52
26
30
42
13
7
Total downtime [hours]
4031 2518 5061 6172 5228 12589 3987 2309
Average downtime per
192
61
97
237
174
300
307
330
failure [hours/failure]
Percentage of total
9,4
5,3
7,3 15,5 13,6
33,5 14,8 17,4
14,6
downtime [%]
Source: Driftuppföljning av Vindkraftverk Årsrapport 1997-2004 and Felanalys, Database Swedpower AB
2005
The most important finding from the overview shown in Table 5 is that the amount of gearbox
failures for the most recent years tends to decrease while the average downtime per failure
increases.
The distribution of the failures according to brand is a way of finding out if some type of
turbine fails more than others. In Table 6, the distribution of gearbox failures for wind
turbines greater than 490kW is shown. The limit of 490kW is chosen since this includes a
large group of Wind World turbines. Smaller turbines are available but since the development
of turbines continue so does the power output and the smaller ones are decreasing as new ones
are installed. It does not look like any type of turbine is worse than the other considering that
the total amount of available data is small. Two types of turbines do stand out more than the
others and it is known that one of them, Neg-Micon, has had various problems especially with
the gearbox [13]. The average number of gearbox failures per turbine for turbines above
490kW is 0,18
33
Reliability performance and maintenance – a survey of failures in wind power systems
Table 6: Distribution of gearbox failures for turbines with a power of more than 490kW
Total number
Average
Total
Brand
Power Number
of installed
downtime
[kW]
of failures downtime
turbines
[h]
[h]
[n]
(2005) [n]
Bonus
600
6
654
Enercon*
500
1
31
GE Wind
Energy/
EnronWind
1425
16
4519
Neg-Micon
600
5
1247
Neg-Micon
750
9
1329
Nordic
1000
1
224
Vestas
500
5
570
Vestas
600
19
7887
Vestas
660
19
7151
Vestas
850
3
370
Vestas
1500
6
5138
WindWorld
490
11
3517
WindWorld
500
6
1174
WindWorld
600
2
940
All turbines
109
34751
Source: Felanalys, Database Swedpower AB 2005
* Enercon E40 is a gearless turbine.
109
31
22
31
Average
number of
gearbox
failures per
turbine [n]
0,17
0,03
283
250
148
224
114
415
376
124
857
320
196
470
319
7
3
20
4
23
56
79
93
16
16
27
43
593
2,29
1,67
0,45
0,25
0,22
0,34
0,24
0,03
0,37
0,69
0,22
0,05
0,18
In Table 6 an Enercon E40 turbine is represented. This type of turbine is a gearless turbine
and the design is different from the other turbines. Instead of the gearbox stage it utilises a
direct driven synchronous ring generator. Direct drive does not have a cost or weight
advantage over conventional geared systems but especially in the PMG (Permanent Magnet
Generator) type of design, it constitutes a simpler power train than the gearbox/high-speed
generator combination and may be more reliable.[14] The reason why this problem is reported
as a gearbox problem is unknown.
In the end the gearboxes are not all that different from each other. In design there might be
differences, but when it comes to the basics they are similar. A gear is a toothed wheel that
works with other toothed wheels to alter the relationship between the speed of the rotor and
the speed of the driven parts, such as the generator. A gearbox is a set of interlocking gears in
some kind of casing, usually cast iron. [15] They all have a containing case, lubricant system
and wheels held in place by bearings.
Table 7 shows the failed subcomponent within the gearbox for turbines with a rated power of
490kW or more. Each type of failure has a specific code assigned to it on the report form.
The letter I stands for gearbox and the subsequent number is the code for what part of the
gearbox that failed, e.g. I-1 is a general failure to the gearbox.
34
Reliability performance and maintenance – a survey of failures in wind power systems
Table 7: Type of gearbox failure
Type of
Component Number of
Average
reported
failures [n] downtime [h]
failure code
(min-max)
I-1
Bearings
41
562 (15-2067)
I-2
Gearwheels 3
272 (57-383)
I-3
Shaft
0
0
I-4
Sealing
8
52 (2-218)
I-5
Oil system
13
26 (1-63)
I-other
Not
44
230 (9-1248)
specified
Source: Felanalys, Database Swedpower AB 2005
Number of
failures,
Cause: B1 [n]
36
2
0
4
5
19
Average
downtime,
Cause: B1 [h]
601
379
0
30
36
299
In Table 7, the last row is made up of unspecified failures. This means that they have been
filed under gearbox failure but no sub component has been specified. It is possible to examine
additional text added in the report and a quick glance at these reveal that about half of the
unspecified gear failures correspond to serious failures that resulted in a replacement of the
whole gearbox. When it comes to I-1 one failure, failures to the bearing, most of them
demanded a total change of the gearbox or the bearings in the gearbox. In Table 7, the last
two columns show category B1-failures. When reporting the failed component in the incident
report it is also possible to put in what caused the failure. A B1 failure is a failure caused by
wear. The majority of the failures to the gearbox are caused by wear and therefore they are
shown here explicitly.
Note: The layout of the report form has changed over the years and presently failure code I-5
appears to have been merged with failure code I-4, see Appendix 1.
35
Reliability performance and maintenance – a survey of failures in wind power systems
5.3.5
Summary of the statistics for Sweden
In Table 8 the most important findings from the investigations of the failure statistics in
Sweden is presented.
Table 8: Summary of statistical findings for Sweden
Findings
Average number of
failures
Average downtime
Most number of
failures
Most amount of
downtime
Longest downtime
per failure
Important findings
from the failures
statistics
Important findings
concerning gears
36
Sweden
0,402 times a year
52 hours per year; 170 hours per failure
1. Electrical system
2. Sensors
3. Blades/Pitch
1. Gears
2. Control system
3. Electrical system
1. Drive train
2. Yaw system
3. Gears
1. Turbines below 1MW show similar failure rate trends, with a
small increase for the first three years of operations and after five
years it decreases.
2. Turbines above 1MW show an increasing failure rate over the
operational years.
1. The amount of failures has decreased for the most recent years
2. The downtime for gearbox failures has increased in recent years.
3. The majority of the gearbox failures are caused by wear.
4. No link between brand of turbine and amount of gearbox failures
can be proved since the amount of available data is small, but two
types of turbines stand out compared to the others.
Reliability performance and maintenance – a survey of failures in wind power systems
5.4 Statistics from Finland
In Finland the data is collected by the research centre, VTT. The data is published in a yearly
report that shows the development of wind power and the performance of the existing plants
within Finland. In Finland there are currently 92 wind power plants, all reporting their
performance to VTT.
Table 9: Amount of turbines used in survey in Finland 2000-2004
Year
2000
2001
2002
2003
2004
Number of turbines in survey
74
92
63
63
64
Average during
2000-2004
71,2
An overview given in the reports show statistics collected between 1996 and 2004 and it
shows the distribution of downtime for failures in different components in the wind power
plant, see Figure 16, [16]. This distribution data is taken directly from the report published in
Finnish and some of the translations were made with the aid of the failure report sheet which
had been translated by personnel at VTT [30].
Distribution of downtime for failures in Finland between 1996-2004
Control System
4%
Other
2%
Break
4%
Unknown
2%
Slipring
1%
Gearbox
32%
Rotorblades
11%
Drivetrain and Hub
6%
Heating
4%
Electrical System
10%
Nacelle
3%
Yaw system
6%
Hydraulics
11%
Generator
4%
Figure 16: Distribution of downtime for failures in Finland 1996-2004 [16]
It clearly shows that the gearbox stands for about one third of the total downtime for all the
Finnish wind turbines. The gearbox is known as difficult to repair and replace hence the great
portion of downtime.
5.4.1
Finnish failure statistics for the years 2000-2004
In Figure 16 the data was based on values from a given pie-chart in the Finnish yearly report
of wind power statistics but by using the given data of failures of specific components found
in the same reports between the years 2000 until 2005 more information about the distribution
of failures can be revealed. For example can frequency of failures for different components
can be found as well as damages to specific subcomponents. With this information mean
downtime for every component, unavailability and other key figures can be calculated.
37
Reliability performance and maintenance – a survey of failures in wind power systems
Figure 17 shows the distribution of downtime per component for the years 2000-2004 and the
result is similar to that of the previously described results in Figure 16 for 1997-2004. The
gears are responsible for a third of all the downtime for the wind power plants. During the
period 2000-2004 the failures related to blades/pitch is high and one of the reasons for this is
a storm which damaged the airbrake-tips of three plants and resulted in over 6000 hours of
extra downtime for the year 2004.
Dis tribution of downtim e, 2000-2004 [%]
Unknown 2,1
Other 2,1
Entire unit 0,0
Structure 6,5
Hub 0,1
Blades /Pitch 21,2
Yaw Sys tem 6,5
Generator 4,4
Hydraulics 11,4
Electric Sys tem 6,4
Control Sys tem 1,7
Mechanical Brakes
2,8
Drive train 0,0
Gears 32,8
Sens ors 2,0
Figure 17: Distribution of downtime for failures in Finland 2000-2004
Source: Tuulivoiman Tuotantotilastot Vuosiraportti 2000-2004
In Figure 18 the distribution of the amount of reported failures is shown. A total of 491
failures had been reported during the period 2000-2004 and most of these failures were
related to the hydraulics. Blades, pitch and gears are also frequently failing according to the
statistics. Any exceptional reasons for these failures have not been found, except for the
earlier mentioned storm in 2004.
38
Reliability performance and maintenance – a survey of failures in wind power systems
Dis tribution of Num ber of failures , 2000-2004 [%]
Other 4,5
Unknown 2,4
Hub 0,6
Entire unit 0,0
Blades /Pitch 14,3
Structure 6,3
Yaw Sys tem 7,3
Generator 6,1
Electric Sys tem 7,7
Control Sys tem 6,9
Hydraulics 22,8
Drive train 0,0
Sens ors 8,4
Mechanical Brakes
3,3
Gears 9,4
Figure 18: Distribution of failure frequency in Finland, 2000-2004
Source: Tuulivoiman Tuotantotilastot Vuosiraportti 2000-2004
A table with all values and averages has been compiled see Table 10. This table shows the
downtime and amount of failure for each component and at the bottom of the table the
averages and totals for the wind power system is shown. For the calculation of averages per
turbine a calculated value for the amount of existing erected turbines with regard to turbines
without gearbox or hydraulics, i.e. Enercon turbines.
A closer look at the table shows that the average wind turbine in Finland has 1,38 failures
every year and the average downtime per failure is 172 hours. The downtime for maintaining
the gearbox is extremely high where an average gearbox failure corresponds to about 600
hours of downtime which is more than twice as much as for the blades/pitch where an average
failure takes about 256 hours to repair. The long downtime for the gearbox makes it a critical
component for the Finnish wind power turbines.
39
Reliability performance and maintenance – a survey of failures in wind power systems
Table 10: Downtimes and failure frequency for Finnish wind power systems, 2000-2004.
Total number
Distribution
Average
Average
Component Total
of downtime, of failures per
downtime per
downtime per downtime
year per turbine 2000-2004 [%] component,
per year
component,
2000-2004 [n]
[h/yr/turbine]
2000-2004 [h] [h/yr]
Hub
Blades/Pitch
Generator
Electric
system
Control
system
Drive train
Sensors
Gears
Mechanical
brakes
Hydraulics
Yaw system
Structure
Average
number of
failures
per year
[n/yr]
Average number
of failures per
year per turbine
[n/yr/turbine]
Distribution
of failures,
2000-2004
[%]
Average
downtime
per failure,
2000-2004
[h/failure]
60
17916
3686
12
3583
737
0,2
50,3
10,4
0,1
21,2
4,4
3
70
30
0,6
14,0
6,0
0,01
0,20
0,08
0,6
14,3
6,1
20
256
123
5427
1085
15,2
6,4
38
7,6
0,11
7,7
143
1431
0
1727
27706
286
0
345
5541
4,0
0,0
4,9
88,0
1,7
0,0
2,0
32,8
34
0
41
46
6,8
0,0
8,2
9,2
0,10
0,00
0,12
0,15
6,9
0,0
8,4
9,4
42
0
42
602
2330
466
6,5
9652
1930
30,6
5495
1099
15,4
5524
1105
15,5
Entire unit
0
0
0,0
Other
1739
348
4,9
Unknown
1735
347
4,9
Total
84428
16886
237,2
Source: Tuulivoiman Tuotantotilastot Vuosiraportti 2000-2004
2,8
11,4
6,5
6,5
0,0
2,1
2,1
100,0
16
112
36
31
0
22
12
491
3,2
22,4
7,2
6,2
0,0
4,4
2,4
98,2
0,04
0,36
0,10
0,09
0,00
0,06
0,03
1,38
3,3
22,8
7,3
6,3
0,0
4,5
2,4
100,0
146
86
153
178
0
79
145
172
40
Reliability performance and maintenance – a survey of failures in wind power systems
5.4.2
Gearboxes in Finland - more failure statistics
Since the gearbox is such an important component for the wind turbine, not just for its
function but also for its availability, some more statistics concerning the gearbox will be
examined.
Table 11 shows the trend for the amount of downtime for gearbox failures between the years
2000 and 2004, and last in the table is also the overall percentage of downtime for gearboxes
during the period 1996-2004.
Table 11: Percentage of downtime for gearbox failures in Finland
Year
2000
2001
2002
Percentage of total downtime
42%
62%
28%
Source: Tuulivoiman Tuotantotilastot Vuosiraportti 2000-2004
2003
0%
2004
0%
1996-2004
32%
The table of gearbox failures shows a downward trend for the percentage of failures. There
are significant differences between the years 2000-2002 compared to 2003-2004. In an
interview with personnel from VTT, they say that the high downtime for the years 2000 to
2002 was due to a smaller wind turbine around 300kW that took a long time to repair. They
also claim that the years 2003-2004 are more “normal” [32]. During this period there has also
been a fire in a turbine. The conclusion of this is that with the small amount of active turbines
and with the exceptional failures that has occurred, one has to use caution when drawing any
conclusion out of these data.
The “gearbox” is used as a general term for a component that constitutes of different parts. An
examination of the subcomponents of the gearbox and the correlated failure statistics is
presented in Table 12. In the table one can see how the distribution of failures looks for the
different subcomponents for the last five years. In the bottom line of Table 12 the data for all
components in the wind power plant is shown for comparison.
Table 12: Overview of gearbox failures in Finland, 2000-2005
Total
Component
Subcomponent Total
number of
downtime
failures per
per
component, component,
2000-2004
2000-2004
[n]
[hours]
Gearbox
Gearbox
23935
35
general
Wheel
2232
4
Shaft
1423
2
Sealing
116
5
Bearings
0
0
Lubrication
system
0
0
Gearbox total
Total: 27706
Total 46
All components Total: 84428
Total: 491
Source: Tuulivoiman Tuotantotilastot Vuosiraportti 2000-2004
Percentage
of total
number of
failures,
2000-2004
[%]
Percentage
of total
downtime,
2000-2004
[%]
Average
downtime per
component,
2000-2004
[hours/failure]
7,1
0,8
0,4
28,3
2,6
1,7
684
558
712
1,0
0,0
0,1
0,0
23
0
0,0
Total: 9,4
Total: 100
0,0
Total: 32,8
Total: 100
0
Average:602
Average:172
Table 12 shows three interesting findings. The first one is that 35 out of 46 four gearbox
failures are reported as a general gearbox failure and the affected subcomponents are seldom
41
Reliability performance and maintenance – a survey of failures in wind power systems
reported. This could mean that the whole gearbox needed to be refitted or that all parts in the
gearbox where effected. When a gearbox breaks down usually all the parts are affected and in
need of some sort of maintenance. The second finding is that no bearing failure is reported.
The reason for this is not investigated here but just commented as odd since in all other
statistics and research the bearings seems to be the number one problem with gearbox failures
[13]. The third finding is that the amount of time that it takes to repair a broken gearbox is
substantial, on average it takes 602 hours to repair.
5.4.3
Summary of the statistics for Finland
In Table 13 the most important findings from the investigations of the failure statistics in
Finland is presented.
Table 13: Summary of statistical findings for Finland
Findings
Average number of
failures
Average downtime
Most number of
failures
Most amount of
downtime
Longest downtime
per failure
Important findings
from the failures
statistics
Important findings
concerning gears
42
Finland
1,38 times a year
237 hours per year; 172 hours per failure
1. Hydraulics
2. Blades/Pitch
3. Gears
1. Gears
2. Blades/Pitch
3. Hydraulics
1. Gears
2. Blades/Pitch
3. Structure
1. The gearbox demands long downtime at failures.
2. The statistical data are not satisfactory due to many anomalies in
the failures and a small population of turbines.
1. The amount of downtime for gearbox failures has decreased for
the most recent years
2. Most gearbox failures are reported as “general failures”
3. No failures to the bearings in the gearbox have been reported
Reliability performance and maintenance – a survey of failures in wind power systems
5.5 Statistics from Germany
The statistics that has been analysed from Germany comes from the WMEP
(Wissenschaftliches Mess- und EvaluierungsProgramm) which is a part of the 250MW Windproject. WMEP is a research programme where some wind turbines are followed for a period
of more than ten years and some even for more than fifteen years. This research is important
since there are no real good long term statistics that show what happens with the wind power
plant after a long period of time. The manufacturers of wind turbines claim that there is a
lifetime of a wind turbine for about twenty years but no wind power plant has yet reached that
age. The drawback of this research is that during the time this measurement programme is in
place, new turbines are being developed and new designs are being predominant. Good
statistics for later design is therefore not available at the moment.
5.5.1
Number of failures
Figure 19 shows the distribution of failures for wind turbines within the German WMEP
research program. In older models, some of the most frequent problems concern the electrical
part of the wind turbine. Connecting a turbine to the grid is not as easy as it sounds because
when you connect to the grid there are certain quality aspects that have to be considered. If a
failure occurs the turbine is not allowed to severely disturb the grid and vice versa. This area
has been researched and is still undergoing important research, but will not be analysed within
this paper. The problem with the electrical components and the grid connection is now more
controlled. In Figure 19 one can also notice that electrical failures, i.e. Electrical system,
Sensors and Control System, represent half of the failures and the other half are due to
mechanical failures [17]
Dis tribution of num ber of failures
Yaw Sys tem
8%
Structure
4%
Hub
5%
Blades /Pitch
7%
Generator
4%
Hydraulics
10%
Mechanical Brakes
5%
Electric Sys tem
24%
Gears
4%
Sens ors
10%
Drive train
2%
Control Sys tem
17%
Figure 19: Distribution of Number of failures [27]
43
Reliability performance and maintenance – a survey of failures in wind power systems
5.5.2
Downtime
In Figure 20 the distribution of downtime per component failure is presented. In this figure
the gearbox, the generator and the drive train stands out as components with the longest
downtimes of about 6-8 days. More frequent failures such as problems with the electrical
system are easily corrected and the downtime for such a problem is less than 2 days.
Distribution of dow ntime
Hub 8%
Structure 8%
Y aw System 6%
Blades/Pitch 10%
Hydraulics 3%
Mechanical Brakes 6%
Generator 17%
Gears 15%
Electric System 4%
Control System 5%
Sensors 4%
Drive train 14%
Figure 20: Distribution of downtime within WMEP [27]
For Figure 19 and Figure 20 the accumulated number of failures and downtime for all the
turbines during the whole research period is shown. Some turbines have been active in the
WMEP programme for more than 15 years.
In Table 15 data from the WMEP study have been examined and calculations of distribution
of failures have been done only for the years between 2004 and 2005 and in some cases
values for 2003 have been available. The amount of turbines that contributes to the data varies
and Table 14 presents the number of turbines that are active for the years 2004-2005 which
have been studied more deeply. The number of participating turbines in the database is more
than the amount of turbines that are funded by the research program; this is because some
turbine owners voluntarily submit data.
Table 14: Number of turbines in survey
Year
Number of turbines
funded by WMEP
Number of turbines
in database
44
2004
564
2005
403
Average 2004-2005
483,5
1080
650
865
Reliability performance and maintenance – a survey of failures in wind power systems
Table 15: Number of failures for German Wind Power plants within WMEP
Component
Total number of failures
Average number of
between 2004-2005 [n]
failures per year
[n/yr]
Hub
Blades/Pitch
Generator
Electric System
Control System
Drive train
Sensors
Gears
Mechanical
Brakes
Hydraulics
Yaw System
Structure
SYSTEM
Average number of failures per year
per turbine [n/yr/plant]
Distribution of failures in WMEP,
2003-2005 [%] (*)
10
374
89
856
450
80
273
190
5
187
44,5
428
225
40
136,5
95
0,01
0,22
0,05
0,49
0,26
0,05
0,16
0,12
2,21
9,81
2,59
27,14
14,63
2,33
8,80
5,72
169
336
220
128
84,5
168
110
64
0,10
0,21
0,13
0,07
5,22
10,57
6,87
4,10
3 175
1587,5
1,86
100,00
(*) Indicates that additional values from 2003 was available and used.
Source: Windenergie report Deutschland 2004-2005
45
Reliability performance and maintenance – a survey of failures in wind power systems
WMEP has presented a summary of annual failure frequency and downtime and this is
presented in Table 16 but these values have not considered the fact that not all turbines are
fitted with gearboxes.
Table 16: Number of failures and downtimes, accumulated values for the whole research period for
WMEP [27]
Component
Number of failures per Downtime per failure [hours] Downtime per
year [n]
year
Hub
Blades/Pitch
Generator
Electric System
Control System
Drive train
Sensors
Gears
Mechanical
Brakes
Hydraulics
Yaw System
Structure
Total/Average
0,11
0,17
0,10
0,55
0,41
0,05
0,24
0,10
85,8
99,4
179,2
36,4
45,8
137,3
35,8
153,3
9,5
17,1
17,5
20,1
18,9
7,4
8,7
15,6
0,13
0,23
0,18
0,09
64,8
28,4
64,6
79,7
8,5
6,5
11,6
7,5
Total: 2,38
Average: 62,6
Total: 149,0
The main conclusions from the German statistics are that the gears, drive train and generators
are critical components for German wind power turbines. The data examined for 2004-2005
also show a lower frequency of failures indicating that the failure frequency is decreasing. A
typical German turbine have 2,38 failures a year (1,86 according to calculations for 20042005) and a failure demands an average of 62,6 hours of downtime.
46
Reliability performance and maintenance – a survey of failures in wind power systems
5.5.3
Summary of the statistics for Germany
In Table 17 the most important findings from the investigations of the failure statistics in
Germany is presented.
Table 17: Summary of statistical findings for Germany
Findings
Average number of
failures
Average downtime
Most number of
failures
Most amount of
downtime
Longest downtime
per failure
Important findings
from the failures
statistics
Important findings
concerning gears
Germany
2,38 times a year (1,86 times a year for 2004-2005)
149 hours per year; 62,6 hours per failure
1. Electrical system
2. Control system
3. Hydraulics, Sensors
1. Generators
2. Gears
3. Drive train
1. Generators
2. Gears
3. Drive train
1. The failure rate seems to decrease in recent years.
2. Half of the failures are electrical, the other half are mechanical.
1. The gearbox demands a long downtime per failure.
47
Reliability performance and maintenance – a survey of failures in wind power systems
5.6 Discussion about the reliability of the statistic data
During the examination of failures in Finland, Sweden and Germany questions about how the
data was gathered was raised. Is the data really reliable? In interviews with the people daily
working in these areas they agreed that there are some issues when it comes to reporting the
failures correctly and also in the willingness to report failures. [29], [28]
When failure statistics are being analyzed one has to consider several important issues:
1. When a major incident happens, for example when a fire starts in the nacelle, the
consequence is severe and the repair and downtime will be significant. Therefore one
cannot just look into failure incident for one year only, but one has to examine the
trends within the failures for several years.
2. Secondly, there is a rapid development of new techniques and different designs within
the wind power industry, thus when looking at statistics one has to know if it is
relevant for the new designs. Some of the infant problems may have been eliminated
or improved. So when examining the statistics one has to know that some of the data
are old and that one has to evaluate the data concerning to the type of design.
3. Most countries collect the data from the wind power production in some way.
Primarily it is the production and generated power that is measured and in second
place comes measurements of failures and maintenance of the wind turbine. These
ways of collecting the data are somewhat similar in theory but vary in practice.
Different parts of the system are put in the statistical database and when later on
compiled into statistics tables one usually group up problems concerning different
components. The way of grouping data differs between countries.
4. Who is collecting the data? Since the type of ownership of the turbines varies
throughout Europe so does also the type of expertise in collecting correct data. Some
owners perform their own service and may not report in the same way as some other
serviceman hired by a wind power generating company.
5. When collecting and examining data, the persons in charge usually eliminate data
from research facilities and correct other data that is not showing up correctly in the
statistics. It is assumed that this is done with the data that is collected and it is usually
stated, but not always.
48
Reliability performance and maintenance – a survey of failures in wind power systems
5.7 Conclusions on the findings in the statistical survey
A comparison of the three countries is possible to some extent. One can see trends and
similarities between the three different countries. A compilation of the findings in this chapter
is presented in Table 18.
Table 18: Summary of statistical findings
Findings
Average number
of failures per
turbine
Average
downtime per
year
Average
downtime per
failure
Most number of
failures
Sweden
0,402 times a year
Finland
1,38 times a year
52 hours per year
237 hours per year
Germany
2,38 times a year
(1,86 times a year for
2004-2005)
149 hours per year
170 hours per failure
172 hours per failure
62,6 hours per failure
1. Electrical system
2. Sensors
3. Blades/Pitch
1. Hydraulics
2. Blades/Pitch
3. Gears
1. Electrical system
2. Control system
3. Hydraulics,
Sensors
1. Generators
2. Gears
3. Drive train
1. Generators
2. Gears
3. Drive train
1.
2.
3.
Longest downtime 1.
per failure
2.
3.
Most amount of
downtime
Gears
Control system
Electrical system
Drive train
Yaw system
Gears
1.
2.
3.
1.
2.
3.
Gears
Blades/Pitch
Hydraulics
Gears
Blades/Pitch
Structure
It is possible to say that the downtime of the failures is similar. The average downtime is less
in Germany compared to Sweden and Finland and that could be explained by a better and
nearer service organisation, but overall the average downtime for a failure is as high as 62172 hours, i.e. 2-7 days. According to the findings each turbine needs to be attended for repair
at least once a year, i.e. 0,4-2,38 times a year. One must also consider that in this survey only
the major incidents are reported and the downtime for scheduled maintenance is omitted. If
the wind power is to be competitive the downtime needs to be shortened and visits to the
turbine should be kept to a minimum.
The gears and the drive train are the components that demand the longest downtime per
failure. The reason for this is that they are big and cumbersome to replace, and replacement
involves equipment such as cranes, etc. Since drive train and gearboxes seldom fails, one
reason for the long downtime could be that spare parts need to be ordered which could
prolong the time for repair.
It is evident that the gearbox is critical to the availability of the wind turbine. A lot of failures
appear and most of them are caused by wear on the mechanical parts. The statistical data
found about the gearbox failures combined with the interviews and all the articles read do all
support this conclusion. The fact that there are problems within gearboxes is also agreed upon
by representatives from insurance companies such as Allianz in Germany, which have made
49
Reliability performance and maintenance – a survey of failures in wind power systems
thorough studies of wind turbines [2]. Damages to the gearbox are not just a problem that can
be fixed by resetting a button. Most gearbox failures will lead to the exchange of parts and
even a refit of the whole gearbox. The underlying reason for this extensive wear needs to be
examined further and some suggestions on what caused the wear are given later in Chapter
6.5.
The wear is somewhat constant but still it is hard find out when it is time to change the
gearbox since so many failures occur. If there was a way of measuring the wear, the
replacement of the gearbox prior to the failure would reduce the downtime for repair. What is
needed is a way of measuring the status of the gearbox.
50
Reliability performance and maintenance – a survey of failures in wind power systems
6 Overview of the Gearbox
The basic gearbox used in any application consists of a containing case, a lubrication system,
and the gears that are held in mesh by axial and radial supporting bearings. What differs
between gearboxes is among many things, the sizes, type and number of gears and bearings
and also the designed load range. [11]
“Wind turbines are one of the most demanding applications for gearboxes, due to
variable loads that are extremely difficult to predict.” … “Current wind turbine gear
boxes are much better than those made just a few years ago” - C. D. Schultz (Chief
Engineer at Brad Foote Gear Works), 2005 [15]
The previous conclusion that the gearbox is troubled with failures is confirmed by the wind
power industry and it is in the area of the gearbox that significant developments in basic
design architecture are now appearing. Different configurations are appearing and it is far
from clear which of the configurations that is optimum. [4] Within gearbox design there are
several different ways to go and the business of gearboxes is in turn also quite complex as it
involves many gear and bearing manufacturers as well. One manufacturer might even rely on
several gearbox suppliers.
6.1 Gearbox design
The function of the gearbox is to change the speed of rotation of one shaft into another
rotational speed for another rotating shaft. The wind turbine gearbox characteristics are
typically; high torque and low speeds.
In Chapter 2.3.5, the gearbox was described like this:
The gearbox transforms low-speed revolutions from the rotor to high-speed
revolutions. To transform the low rotational speed of about 30 rpm to 1500 rpm,
usually three stages are needed. The design of the gearbox is subject to constant
changes. At the moment a common solution is to use a planetary stage gear which
has a feature of being very compact. Via a high-speed shaft the gearbox is then
connected to an electric generator.
The gears can be designed differently, for example the teeth can be straight spur gears or
helical gears. Helical gears have a slightly slanted toothing. The slanted design brings more
teeth into contact, giving a greater contact ratio and the sharing of the load is greater than that
of spur gears. Another way of designing the gearbox is by using planetary gears. A planetary
gear is a special setup where a centred sun wheel is connecting to three planetary wheels
inside of a ring wheel. In this design three gearwheels are always engaged and supporting
each other at the same time and the benefits is a smaller and more compact design of the
gearbox. [5] These were just two examples of design choices which come with both pros and
cons. Thus a complex set of design parameters and limitations make the gearbox selection
process a trial. Just as gear-type selection is complicated for wind turbine gearing, so too is
the choice of materials. [15]
The design of gearboxes constantly changes, but to understand the terminology used for
gearboxes some of the design basics will be explained.
51
Reliability performance and maintenance – a survey of failures in wind power systems
6.1.1
Gearbox stages
A transformation of rotation from for example 30 rpm to 1500rpm is not done in a single
stage. Instead a gearbox uses several stages to stepwise alter the speed. In this case three
stages would be used. The sizes of the stages differ but the ratio is usually about 1:4-1:5, so
when using three stages the total ratio becomes three times the ratio for one stage. The first
stage, where the rotor is connected to the gearbox, is usually referred to as the low-speed
stage, the next one is the intermediate stage and the last stage is called the high speed stage.
The high-speed stage is then connected to the generator.
6.1.2
Parallel stage and planetary stage
The stages used in the gearbox can be of many different configurations but what is commonly
used is a combination of a planetary gear stage and a parallel gear stage. The parallel gear is
simply two gearwheels situated next to each other where the rotational energy is transformed
to the other gear, see Figure 21
Figure 21: Parallel gear
The planetary gear is somewhat more complex but has some advantages. The planetary gear
consists of a Sun gear in the middle and three planet gear evenly placed around the Sun gear,
see Figure 22. Around the planets is a Planet ring (or Planet carrier) which holds the gears in
place. The Planet ring is also toothed on the inside where it carries the planet gear.
Planet Ring
Sun Gear
Planet Gear
Figure 22: Planetary gear
One of the great advantages of the planetary gear is that the construction of the gearbox can
be made very compact and weight can be reduced. This is due to the fact that there are three
gears in contact with the sun wheel all the time and they share the stresses and the forces.
There will be a proper torque without extra linear forces which would appear in a parallel gear
stage. One disadvantage with the planetary gear stage is that a more complex design makes
the gearbox more vulnerable.
52
Reliability performance and maintenance – a survey of failures in wind power systems
6.1.3
Teeth and spurs
The gearwheels themselves have different appearances as the spurs or teeth on the gears can
be designed differently. Two different types will be shown here, the straight spur and the
helical spur. The straight spur is a traditional gearwheel with spurs in a straight angle from the
rotational direction. The helical gear uses slanted spurs that has a longer contact area
compared to a straight spur. With a greater surface area the sharing of forces will be greater
and this will also reduce noise coming from the operation of the gearbox.
Figure 23: Different types of teeth. Left: straight spurs. Right: helical spurs
6.2 Gearbox operating conditions
In traditional power plants gears operate at constant speed, but gears in the state-of-the-art in
wind turbines have to cope with partial load and variable speed. In addition, the gearbox
torque is dynamic due to wind speed turbulence. Yet another extraordinary load comes from
idling, which is characterised by heavy torque variations at low speeds and extreme loads
such as generator short circuit or emergency stops in which the gearbox can be loaded with
several times the rated torque.
The above described characteristics of the gearbox for the wind turbine put extensive
demands on lubrication of gears and bearings and the damage process is modelled with a
break in period followed by the operations period and then ended with the breakdown period
as depicted in the bathtub curve in Figure 3 (see Chapter 3.2.2).
The operating conditions of ordinary power generating generators are characterized by high
input speeds at relatively low torques. In contrast to this wind turbines are operating at very
low speeds between 7 to 30 rpm and relatively high input torque. These torque levels cause
highly loaded gears and bearings and produce severe stresses inside of the tribological system.
Tribology is the science of surfaces in contact during motion and friction. The friction
between the surfaces result in small particles falling off, also known as micro pitting. The
damage process of micro pitting is like a typical wear out process, which takes place in the
chronological order of break in, operation, and breakdown.
6.3 Gearbox development
Mass-produced wind turbines have been improved over the past 10 to 15 years. For the
gearbox some of the improvements have been [18]:
•
•
Gearboxes have been developed in relation to their ability to resist impact caused by
changing speeds
The gear wheels in the gearboxes now have inclined toothing to increase power
transmission and reduce noise.
53
Reliability performance and maintenance – a survey of failures in wind power systems
•
6.3.1
The gears now have oil coolers to extend the intervals between oil changes and
increase the useful lives of the gears.
The future for gearboxes
In the future it is likely that for larger machines, with a power of more than 3MW, an
additional gearbox stage will be required. Therefore, the complexity of the gearbox may be
increased beyond those currently being used or designs based on a lower generator speed
(rpm) may be used to compensate for this effect. [14]
Throughout the development of the modern wind turbine there have been periods when the
frequency of failure of gearbox components has been above normal, acceptable levels. The
gearbox is one of the more costly components and there is always a large incentive to reduce
costs. [14] The cost for replacement of a gearbox is in the order of about 3 MSEK [27].
Gearboxes for use in offshore environments may be more complex and the increased
complexity may lead to increased probability of failure.
There are only a small number of failure modes that can be rectified on site. Therefore, to
repair a failed gearbox will entail the removal of the unit from the turbine with significant cost
and time implications. The above issues suggest that there is a reasonable possibility that
direct drive technologies may prove more attractive than they currently appear to be in the
onshore market. (Note: These assumptions are based on Germanischer Lloyd’s engineers'
experience in due diligence and are not attributable to any specific published source.
Germanischer Lloyd is a German insurance company that tests and certifies wind turbines.)
[14]
6.4 Gearbox wear and failures
“Gearbox wear and failure usually result form wear and failure of the primary load
carrying elements such as shafts, gears and bearings.” - Rao 1996, [11]
As earlier mentioned the basic gearbox consists of a containing case, a lubrication system, and
gears that are held in mesh by axial and radial supporting bearings. What differs between
gearboxes is among many things, the sizes, type and number of gears and bearings and also
the designed load range. [11] Because there are so many configurations of gearboxes,
determination of health and detection of wear and failures is limited to a few basic fault
symptoms common to nearly all configurations. This in turn tells us that the estimation of the
condition and remaining lifetime of a gearbox is an inexact science. There is quite a distance
between measuring the symptoms to coming up with a definition of a fault type and its
seriousness. [11] This step is highly interconnected with the gearbox type and its operating
parameters.
6.4.1
Wear process
The wear out is extensive initially during the break in period but during the operations period
the wear process of the gearwheels is constant. This particular wear out process is based on
research on micro pitting. The micro pitting is significantly higher in the low-stage gear and
in fact hardly noticeable in the intermediate and high-speed stage. The profile shape deviation
in the sun gear is between 25-42 micrometers, in the planet gear this is 14-25 micrometers and
54
Reliability performance and maintenance – a survey of failures in wind power systems
in the intermediate and high-speed stage the deviation is less than 1 micrometer. The
deviation is a way to measure the wear of the gears according to classification in grades. The
higher grade, the greater is the deviation of the profile. Gear tooth accuracy is graded
according to DIN 3962. After 10 years of service time, gear tooth accuracy decreases from
grade 5 (new condition) to grade 8 for the planet gear and grade 9 for the sun gear. These
quality grades could cause unacceptable gearbox noise.
Experience from gearboxes in hydropower states that as a rule of thumb one can observe
damage to a gear at least three months prior to failure, given that the machinery is in constant
operation and is undergoing normal aging.
Gearboxes of wind turbines are normally designed for a lifetime of 20 years. The service
strength calculation of the toothing on the gearwheels is made by internationally accepted
standards, ISO 6336. In the gearbox low speed and intermediate stage appear relative slow
peripheral speeds, which are at the limits of the validity range of ISO 6336. These gear
toothings are often damaged by micro pitting as a result of insufficient lubrication. [19]
6.5 Causes for gearbox failures
The causes for gearbox failures are not fully examined. The reason for this is that the first
generation of gearboxes was just industrial gearboxes used in other applications. These
gearboxes were not practical for the dynamic loads that face a wind power turbine. After that
specially designed gearboxes for wind power were developed. The development of the wind
power business literally exploded and many new concepts and designs were born. In the wake
of this expansion came the failures that have become evident for the last years. The cause for
these failures can be many things but a few of the reasons are listed below.
6.5.1
Possible cause: Misalignment
”Alignment is the key to many wind energy installations. It has an impact on virtually
every component within the turbine assembly, from the tower itself all the way
through to the shafts within the gearbox and the gearbox/generator coupling.” Focus (SKF magazine) 2004, [20]
The use of laser optical systems for the alignments of shafts has become state-of-the-art over
the last few years in many businesses. The alignment of gear and generator in wind power
plants present additional problems such as: [21]
•
•
•
•
•
The mounting of the sensors is difficult due the limited space.
The deformation of the nacelle plays a role in alignment. Any change in wind
direction and speed will influence the alignment results.
For some wind turbines the operating mode may vary and then the gear and the
generator may be in different positions to one another during operations.
The generator may sink over time due to aging of the vibration damper.
The alignments must be done during operating states. It is possible do perform the
alignment during a standstill but then there is a question if it is an optimum for a
longer period or for different operating states.
55
Reliability performance and maintenance – a survey of failures in wind power systems
6.5.2
Possible cause: Bearings not specified in documentation
In a report from Elforsk [2], a problem with incorrect documentation was one of the possible
causes for a failure in a Vestas turbine. The type of bearings that should be used in the wind
turbine was not specified and there existed no documentation of what kind of bearings that
was installed in the gearbox and the generator. This is a requirement that the manufacturer
should have had for its sub-supplier. [2]
6.5.3
Similar problem within hydropower
The causes of gearbox failures are not only present within wind power. The possible cause of
failures has been found in other applications. A report on gearbox failures in hydro plants
states similar causes of failure [22]. Most of the damages concerned the teeth of the gearwheel
and the bearings. The reason for this was mainly due to misalignment during assembly,
inappropriate selection of oil quality and underestimated oil filters. The same report surveyed
twenty gearbox failures. 13 out of these 20 failures where related to tooth damage or bearing
failure. 9 out of these 13 failures occurred during the first five years of operation. [22]
6.6 Conclusion on gearbox failures
A lot of the wear process is related to inappropriate selection and testing of part in the
gearbox, the bearings used and the alignment of the components. Gears and bearings seldom
break down spontaneously. Instead they are subjects to a wear process. Today there are many
methods of monitoring the gearbox and the closely connected bearings. The problem that still
appears to be unsolved is the exact wear process for gearboxes within wind turbines.
56
Reliability performance and maintenance – a survey of failures in wind power systems
7 Condition Monitoring Systems
This far in the thesis it is established that the gearbox is the component that causes long
downtimes and cost a lot to repair. The previous chapter discussed more about gearboxes in
general and also how the gearboxes wear down. Now it is finally time to focus on the second
question that this thesis work stipulates;
Is it possible to use a condition monitoring system and is it a good way of decreasing the
amount of maintenance for this or these components?
In other words, what is needed is more knowledge about how to monitor the gearbox and its
bearings and more information to see if condition monitoring is applicable to these
components.
7.1 Benefits of a Condition Monitoring System
In Chapter 4, the use of condition based maintenance was discussed. Procedures for
measuring the condition of the components in the system are necessary for the use of
condition based maintenance and by using a condition monitoring system which constantly
oversees the system this is accomplished. The implementation of a monitoring system will
lead to some new benefits because of its characteristics. These benefits are listed below in
Table 19.
Table 19: Characteristics of condition monitoring systems [3]
Characteristics
Early warning
Identification of problem
Continuous monitoring
Advantages
Avoid breakdowns.
Better planning of
maintenance.
Right service at the right
time.
Minimizing unnecessary
replacements.
Problems resolved before the
time of guarantee expires.
Constant information that the
wind power system is
working.
Benefits
Avoid repair costs.
Minimize downtime.
Prolonged lifetime.
Lowered maintenance costs.
Quality controlled operations
during time of guarantee.
Security. Less stress.
The primary characteristic of the CMS is that it can detect problems in the system at an early
stage. An early warning will give the personnel in charge of the maintenance time to plan the
repairs and if necessary order parts for replacement. The ability to identify the problem is
useful for making the right service task at the right time and the ability to predict where the
problems stem from will help in identifying which parts to replace and also possible causes
for the failure.
57
Reliability performance and maintenance – a survey of failures in wind power systems
7.2 Insurance and CMS
Recent developments in the German wind power industry give another reason for installing a
condition monitoring system. The German wind power industry has for some time been faced
with a so-called ‘revision clause’ by the insurance companies. This means that the insurance
companies require that the complete drive train of the wind power plant has to be overhauled
after 40.000 hours of operations or every fifth year at the latest. The exception to this clause is
if a condition monitoring system recognized by the insurance company is installed. The
leading insurance companies within wind power have created requirements for condition
monitoring systems on wind power plants. The different monitoring systems are tested
according to these requirements and then certified. [21]
Experience show that if a bearing is replaced after a failure the cost for the repair will be
greater than if it was replaced before a breakdown. [3]
In connection with the expiry of the manufacturer’s warranty period it is important for the
owner of the wind turbine to get an overview of its state of maintenance in time for him to
keep the deadlines stated in the warranty conditions. The assessment of the state of
maintenance of the wind turbine should be performed by an impartial expert. [18]
7.3 Condition monitoring in general
Modern type wind energy converters are based on rotational components. Therefore,
measurement of vibration on component housing and structural oscillation will yield data for
the calculation of the characteristics of the wind power turbine. [23] The sensors measure the
acceleration at different places in the turbine. By measuring the acceleration and then
integrating it once or twice one obtains the velocity and the displacement. Components that
vibrate are a sign of malfunction and by simply looking at the displacement of the component
the vibration can be measured.
Another way of analyzing the data from the sensors is by looking at frequency spectrums.
Vibration and oscillation data time series are analyzed and evaluated using spectral analysis
algorithms. These algorithms are based on the Fast Fourier Transform (FFT) functions, which
are common in digital data evaluation. [23] These spectrums will tell you which vibrations are
caused by the ordinary rotating parts in the wind energy converter and also which vibrations
are caused by potential wear or damage on the turbine, the bearings or the gears.
When damage occurs to a bearing, small vibrations occur in the housing of the bearing. The
frequency of the vibrations is depending on the revolution of the shaft but also on the type of
bearing. If one has exact knowledge of what type bearing is used and the speed of revolution
one can analyze the frequency spectrum and thereby tell what part of the bearing that has been
damaged. [3]
The spectrum analysis can also identify alignment problems which are claimed to be a major
factor of shortening the lifetime of the turbine. The modern CMS equipment and software are
now able to analyze the data and give a hint to what might cause the problem. (A potential
problem is that all sites are not equal and that the system parameters have to be set
accordingly. Another issue is that some analyses require an expert to establish a correct
setup.)
58
Reliability performance and maintenance – a survey of failures in wind power systems
7.4 Condition monitoring for gearboxes
The area of Condition Monitoring Systems can be viewed as two separate fields of
technology. On one hand as sensor technology and on the other hand as a diagnostic and
condition monitoring technology. [11] In this thesis we will not focus on the technology of the
sensors, but more on what these sensors are capable of measuring and what these
measurements tell us.
7.4.1
What is possible to be measure?
When applying a condition monitoring system on the bearings and gearbox there are different
parameters to examine. In Table 20, a selection of monitoring methods to use when selecting
what parameters to survey is shown.
Table 20: Condition monitoring method selector [7], [11]
Component
Vibration analysis
Noise analysis
On-line debris analysis
Debris analysis
On-line oil condition monitoring
Oil condition analysis
Water in oil detection
Optical detection systems
Optical alignment systems
On line pressure monitoring
On-line temperature monitoring
Thermal imaging
Stress/Strain analysis
Erosion/Corrosion monitoring
Performance monitoring
Bearings
●
●
●
●
●
●
●
●
●
●
-
Gearbox
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
But just measuring the gearbox and the bearings is not sufficient. The problems with the
gearbox will still be present unless some more actions are taken. Applying a condition
monitoring system also includes applying new methods for maintenance planning.
“The output of a condition monitoring programme is data. Until action is taken to
resolve the deviations or problems revealed by the programme, plants performance
cannot be improved. Therefore, a management philosophy committed to plant
improvement must exist before any meaningful benefit can be derived.” - Davies,
1998, [7]
Periodic monitoring is a prerequisite for certification and these inspections are carried out
with checklists. The turbine is examined by visual inspection and for the drive train including
the gearbox these are a few of the thing the inspectors look for; leakage, unusual noise,
condition of the corrosive protection, greasing, pretensioning of bolts, condition of the gearing
and possibly an oil sample.
59
Reliability performance and maintenance – a survey of failures in wind power systems
7.4.2
Problems with similar measuring environment
Measuring data from the vibration sensors are dependent on wind speed, wind turbulence,
wind directions, temperature, power output, etc. [3] In other words; the data is only useful in
comparison with similar data. Hence a connection with other operational parameters needs to
be done before any analyses can be done.
7.4.3
Measuring techniques
The key observables for wear and failure in gearboxes are [11]:
• Increasing noise and vibration.
• Generation of abnormal sizes and amounts of metallic debris.
• Increased temperature due to increased power losses within the gearbox.
In other words, these are minimum amount of parameters we need to measure to have an
acceptable monitoring system for the gearbox.
“No single surveillance technique or system is suitable to monitor all the aspects of a
complex assembly; rather several techniques may have to be employed, each
dedicated to a specific area, and integrated on the machine. In addition some
components of the machine require monitoring online in real-time as the process
proceeds while others can be satisfactorily controlled offline out of real-time and on
a periodic basis.” - Davies, 1998 [7]
The performance monitoring is mainly aimed at using trend analysis to predict failure, but it
can also be used to provide information about the location of actual or potential degradation
failures. The backside of a complex data acquisition system is that it may rather cause than
reduce the machinery reliability problems unless carefully designed.
Since the complete understanding of component failure mechanism involved is absent as is
the integration of suitable monitoring techniques. Some people claim that a single,
comprehensive, condition monitoring package for use on commercial machining systems is
not yet available. [7][7] The CMS will have a greater effect as soon as it monitors several
sources, e.g. oil, vibration and temperature etc. [30]
“Current vibration diagnostics techniques do not tend to produce a one to one
correlation between signal analysis and fault type, severity or even fault location.
This fact leads to the need for human expert intervention in the vibration diagnostics
process to assure correct fault identification and avoid false calls.” – Rao, 1996 [11]
The corroboration of fault detection by integrating more than one detection technology serves
to reduce dependence on human experts and reduce false calls and missed faults. [11]
As a conclusion to what Davies [7] and Rao [11] argues it can be summarized as; too many
measuring points is not good as the complexity reduces the reliability and we should not use
too few either as the complexity of the analysis needs input from several sources.
60
Reliability performance and maintenance – a survey of failures in wind power systems
7.4.4
Vibration monitoring
“Vibration is ubiquitous. Vibration is defined as a periodic motion about an
equilibrium position. Any systems which possess the inherent properties of inertia
and stiffness oscillate about its equilibrium position, when perturbed by an outside
force.”…“When vibrations reach unacceptable levels, wear and tear processes are
accelerated, which in turn may trigger various failure mechanisms.” - Rao, 1996
[11]
Vibration analysis is the dominant technique used for measuring the condition of mechanical
equipment. The technique examines noise and vibrations from the machines. This technique
started to develop during the 1960s and 1970s and by the early 1980s the instruments and the
analytical skills required to analyse the vibration and noise were fully developed. These
techniques have proven to be very reliable and accurate in detecting abnormal machine
behaviour. [7]
7.4.5
Wear debris analysis
In the gearbox the gears produces small particles during the wear. The friction and the tear
between the gears makes the particles come loose. These particles are called wear debris and
as they come loose they mix up with the lubricating oil. By examining the wear debris one
can find out about the current status of the gearbox.
Wear debris analysis is undertaken in different ways but generally the particles are
categorized in terms of their quantity or concentration, size, morphology and composition.
The associated wear characteristics are the severity, rate, mode and source of the wear. The
oil analysis can be utilized as a part of a proactive maintenance strategy, but wear debris
analysis can only be used to monitor active primary wear. [7]
In Figure 24, the components of a wear debris analysis shown. Samples are taken from the
magnetic plug in the gearbox which collects particles and from the oil filter and also an oil
sample is collected. The analysis will later show the features of the collected particles. The
features are the ones described earlier, quantity, size, morphology and composition. The
features in turn will form the characteristics of the wear and by examining the characteristics
one might find out what part of the gearbox the wear debris stems from and possibly find out
what caused the increased wear.
61
Reliability performance and maintenance – a survey of failures in wind power systems
Trend
monitoring
On-line
measurement
Magnetic
plug
Oil
filter
Oil
sample
Off-line
measurement and
analysis
Particle features
Quantity
Severity
Size
Rate
Morphology
Mode
Composition
Source
Wear characteristics
Figure 24: Components of wear debris analysis
Examples of typical wear debris produced by machines with rolling bearings and gear teeth,
which undergo a non–conformal rolling–sliding type of contact situation, are ferrous particles
of varying shapes and sizes of between 10-1000µm.
To make any conclusion about the changes in the gearbox wear status one has to take samples
from the gearbox oil. Suggestions say that samples of lubricants have to be taken from the
gearbox monthly if the conditions are heavy and with high temperatures. Under normal
operating conditions one can examine samples once every 2 or 3 month. [7]
Oil cleanliness is essential for the health of the machine components and this is especially
important for gearboxes and the bearings. If the oil is clean it reduces the wear and tear of the
gears.
7.4.6
Online Oil Analysis
In applications where failure modes develop rapidly and where accessibility is limited, an
online real-time oil debris monitoring may be desirable. The technology for on line detection
can be broadly divided into three basic technological categories [11]:
62
•
Electromagnetic sensing – this technology is based upon sensing the field disturbance
caused by the presence metallic particles. It should be noted that this technology is
sensitive to environments associated with electromagnetic fields, such as generators.
•
Flow or pressure drops sensing – this technology uses a filter to capture debris. As the
filter gets clogged, sensors can measure if the flow of the oil decreases or if the
pressure drops.
Reliability performance and maintenance – a survey of failures in wind power systems
•
7.4.7
Optical debris sensing – this technology have been used in offline mode for some time
but with recent advances in laser optics and image processing an experimental online
solution has been developed.
Setting the state of condition
Diagnostics equipments manufacturers prefer fault levels based upon past experience with
similar gearboxes, but the decision on level of fault severity that represents ‘failure’ will be
uniquely defined by the particular gearbox application.
“The setting of limits requires diagnostic history, including tear down results, and is
generally an iterative process that converges on a ‘best economic fault threshold’
after several months of operation.” - Rao 1996, [11]
What needs to be considered as well is the total cost of the failure. For example: The problem
of setting the correct levels is not just ‘notify the operator when there is 10 hours until
failure’, but more a question of ‘is it worth the cost of two man weeks of labor and the cost of
replacing the bearings for this small deviation in vibration?’. [11] Adequate information
required to determine optimum task intervals and applicability of age limits can only be
obtained from age exploration after the equipment enters service. [25]
“To adapt a monitoring system to be fit for wind power is a difficult application. This
is due to the availability, low rpm, nothing is stable in the construction, all measuring
equipment is effected by the circumstances at the time of measuring, wind,
temperature, power output, direction of the wind.” – J. Hoflin 2006, [26]
The wind turbines are subject to many irregular forces and the operating conditions vary as
wind direction and weather conditions change. Hence it will be necessary to have an extensive
“run in” period for each turbine until correct fault levels have been identified.
7.5 Conclusions about condition monitoring for gearboxes
In addition to what was mentioned in the previous conclusion about gearboxes, gears and
bearings seldom break down spontaneously. Instead they are subjected to a wear process.
Today there are many methods of monitoring the gearbox and the closely connected bearings.
The problem that still appears to be unsolved is the exact wear process for gearboxes within
wind turbines. The manufacturers of condition monitoring systems will not explicitly claim
that their products can predict the lifetime of the components but they are using terms as “risk
of failure” [30]. Therefore it is impossible to set any definitions of the different stages of the
wear and consequently it is difficult to set up schedules for maintenance based on the actual
condition of the gearbox. The available equipment on the market today can tell when a preset
limit for the condition is reached and can warn the user of a possible failure, but what the
system cannot do is to fully predict the remaining lifetime of the component. To be able to
predict a lifetime close to the real one a lot of measures and comparisons with similar systems
in similar environments is required.
Finally looking back at the second stated question for the thesis;
Is it possible to use a condition monitoring system to supervise the critical components and is
CMS a suitable tool for decreasing the amount of maintenance for the wind power system?
63
Reliability performance and maintenance – a survey of failures in wind power systems
The answer for the first part of this question is; yes, it is possible to use condition monitoring
to measure the state of the gearbox although the definition of states is dependent on turbine
and site specific conditions. For the second part of the question the answer is; yes, it is
possible to use a condition based maintenance strategy with the use of condition monitoring
of the gearbox. The monitoring system may not be able to predict the exact time of failure but
the prediction is good enough to improve the efficiency of preventive maintenance. The
predictions will aid in adjusting the maintenance closer to a condition based maintenance
program compared to a preventive maintenance program, thus decreasing some of the
maintenance costs.
64
Reliability performance and maintenance – a survey of failures in wind power systems
Closure
8 Conclusions and future work
8.1 Conclusions
In this work it has been shown that the gearbox is a critical component concerning both
failure frequency and downtime for the wind power industry within three different countries.
The frequency of failure is not as high compared to other components but the downtime is
much longer than any of the other part of the wind turbine.
Findings
Average number
of failures per
turbine
Average
downtime per
year
Average
downtime per
failure
Number of
gearbox failures
per year
Average
downtime per
gearbox failure
Most number of
failures
Sweden
0,402 times a year
Finland
1,38 times a year
52 hours per year
237 hours per year
Germany
2,38 times a year
(1,86 times a year for
2004-2005)
149 hours per year
170 hours per failure
172 hours per failure
62,6 hours per failure
0,045
0,15
0,1
256,7 hours
602 hours
153,3 hours
1. Electrical system
2. Sensors
3. Blades/Pitch
1. Hydraulics
2. Blades/Pitch
3. Gears
1. Electrical system
2. Control system
3. Hydraulics,
Sensors
1. Generators
2. Gears
3. Drive train
1. Generators
2. Gears
3. Drive train
1.
2.
3.
Longest downtime 1.
per failure
2.
3.
Most amount of
downtime
Gears
Control system
Electrical system
Drive train
Yaw system
Gears
1.
2.
3.
1.
2.
3.
Gears
Blades/Pitch
Hydraulics
Gears
Blades/Pitch
Structure
It is therefore essential to be able to quickly replace or repair this component after failure. The
use of a good maintenance plan together with the possibility to predict the failure is a way of
decreasing the impact of a gearbox failure.
The Condition Monitoring System provides a tool for predicting failure of the gearbox. This
solutions is applicable to any kind of turbine and is a good tool for predicting the condition of
65
Reliability performance and maintenance – a survey of failures in wind power systems
the machinery, though it cannot be emphasized enough that the use of condition monitoring
also require a well functioning maintenance program.
Another important finding form the thesis work is that failures within bigger wind turbines,
above 1 MW, have a higher frequency of failure than smaller and older turbines. The trends of
small turbines is that the frequency of failure decreases or is kept constant with age and the
characteristic incline in failures that the bathtub describes have not yet been identified for
these turbines where some have been active for up to 19 years. The trend for the big turbines
show on an increasing rate of failure, but the amount of statistical data is yet not satisfying to
fully draw that conclusion.
The questions for the thesis where;
1. What component or components are most critical in the wind turbine when it comes to
number of failures and the resulting downtime caused by these failures?
2. Is it possible to use a CMS to supervise these critical components and is CMS a
suitable tool for decreasing the amount of maintenance for the wind power system?
The answers to these questions have been found and it is clear that the gearbox is one of the
most critical components when it comes to which component that affects the downtime
mostly. It was also clarified that condition monitoring systems of today are able to supervise
the gearbox adequately.
The theoretical implications of using a condition based maintenance together with the
condition monitoring systems shows on great benefits and the overall conclusion is that the
use of CMS is beneficial when it comes to reducing the amount of failures to the gearbox and
also when it comes to scheduling the preventive maintenance.
8.2 Future work
The area of implementing condition monitoring sensors for wind power plants is new and
unexplored. There are more areas to focus on but during this research it has been identified
that the use of statistical data is useful and that a thorough research on the original data source
or database can reveal more than published reports which usually cannot cover all aspects. For
example, the investigation of development of failures versus operational year have yielded
information of a higher frequency of failures for larger turbines compared to smaller ones
An interesting study would be to follow specific groups of turbines of the same brand and see
how much the differences are within the group. Another study of interest is the
implementation and effects of condition monitoring systems on the failure frequency and the
downtime.
66
Reliability performance and maintenance – a survey of failures in wind power systems
9 References
9.1 Literature
[1]
L. Bertling, “Pre-study on reliability-centered maintenance for wind power systems with focus on
condition monitoring systems”, KTH school of electrical engineering, project plan for Elforsk project
2356, 2005
[2]
Elforsk informerar, ”Vindkraft 2/05 Drift och underhåll av vindkraftverk”, Elforsk, 2005
Available at: www.vindenergi.org, 06-05-02
[3]
K. Jonasson, Tillståndsövervakning av vindkraftverk – Utvärdering av system utfört av SKF Nova,
Elforsk rapport 01:30, 2001
Available at: www.elforsk.se
[4]
Wind Energy – The facts, H. Chandler (ed.),
European Wind Energy Association 2003
Available at: www.ewea.org 06-05-02
[5]
H. Stiesdal, “The wind turbine components and operation”, Bonus Info, newsletter special issue, Bonus
Energy A/S, Brande, Denmark 1999
[6]
System Reliability Theory, M. Rausand, A. Hoyland,
Hoboken: John Wiley & Sons 2004, ISBN 0-471-47133-X
[7]
Handbook of condition Monitoring, A. Davies,
London: Chapman & Hall 1998, ISBN 0-412-61320-4
[8]
Reliability Theory with application preventive maintenance, I. Gertsbakh,
Berlin: Springer-Verlag 2000, ISBN 3450-67275-3
[9]
Driftuppföljning av Vindkraftverk, Årsrapport, 1997-2004, Elforsk
N.E. Carlstedt, C. Szadkowski, C. Karlström
Elforskrapporter: 98:4, 99:6, 00:11, 01:16, 02:20, 03:12, 04:19, 05:11.
Available from: www.elforsk.se
[10]
Underhåll terminologi - Maintenance terminology, Svensk Standard SS-EN 13306,
Stockholm: SIS Förlag AB 2001
[11]
Handbook of Condition Monitoring, B. K. N. Rao (ed.),
Oxford: Elsevier Science Ltd 1996, ISBN 1-85617-234-1
[12]
Felanalys, Database of failures for Swedish wind power turbines 1997-2005,
used with permission from Swedpower AB.
[13]
D. Robb, L. Harrison, “The role of bearings in gearbox failure”, Windpower Monthly, Nov 2005
Knebel, Denmark, ISSN: 109-7318
[14]
Concerted Action on Offshore Wind Energy in Europe, Duwind 2001:006, Delft University Wind Energy
Research Institute, 2001
67
Reliability performance and maintenance – a survey of failures in wind power systems
[15]
D Robb, “Gearbox design for wind turbines improving but still face challenges”,
Windstat Newsletter, Issue: Summer 2005. Vol. 18. No 3, Forlaget Vistoft, Denmark
[16]
Tuulivoiman Tuotantotilastot Vuosiraportti, 2000-2005, VTT, Espoo, Finland
H. Holttinen, T. Lakso, M. Marjaniemi
Available from: www.vtt.fi
[17]
B. Hahn, M. Durstewitz, K. Rohrig, “Reliability of wind turbines – experiences of 15 years with 1500
WTs”, Institut für Solare Energieversorgungstechnik ISET, Kassel Germany, 2006
[18]
C. Jakobsen, H.Reymann-Carlsen, J Boogaard, A Martin Martin, N. Kragelund, B. Balschmidt, “IMIAInsurance of Wind Turbines”, Danish Insurance Association, IMIA-WGP5(99)E, July, 1999
[19]
J.B. Franke, R. Grzymbowski, “Lifetime prediction of gear teeth regarding to micropitting in
consideration of WEC operation states”, Germanisher Lloyd WindEnergie GmbH, Hamburg, Germany
2004
[20]
P. Burge (ed.) “SKF reliability systems”
Focus, issue 70, 2004, SKF (U.K) Limited, Luton
(Focus is a magazine published by SKF)
[21]
Telediagnose.com, the condition monitoring magazine of Prüftechnik AG and Flender Services GmbH,
“Aligning drive trains in wind power plants”, issue no. 5, Ismaning/Herne, Germany 2003
Available at: www.telediagnose.com
[22]
Å. Grahn, P. Forsell, S. Olsson, S.Lasu, Växlar i vattenkraft,
Elforsk rapport 94:06, 1994
Available at: www.elforsk.se
[23]
J. Giebhardt, P. Caselitz, ISET; J. Rouvillain, MITA Teknik, DK; T. Lyrner, Nordic Windpower,
Sweden; C. Bussler, Plambeck Neue Energien; S. Gutt, Brüel & Kjaer Vibro; H. Hinrichs, Overspeed; K.
Gram-Hansen, Gram&Juhl, DK; N. Wolter, Deutsche Montan Technologie; G. Giebel, Risø, DK,
”Predictive Condition Monitoring for Offshore Wind Energy Converters with respect to the IEC61400-25
standard”, DEWEK 2004, Wilhelmshaven, Germany,
Available at: www.iset.uni-kassel.de/osmr/
[24]
Product specification and information concerning Vestas V90,
Available from: www.vestas.com, 2005-12-16
[25]
P.J. Quinlan, “Reliability centered Maintenance applied to wind park operations”,
Proceeding of Sixth ASME Wind Energy Symposium, Dallas, USA 1987
[26]
Windenergie report Deutschland 2004-2005,
M. Durstewitz (ed.), Institut für Solare Energieversorgungstechnik (ISET), Kassel, Germany
68
Reliability performance and maintenance – a survey of failures in wind power systems
9.2 Interviews
[27]
Berthold Hahn, Head wind energy use, Institut für Solare Energieversorgungstechnik (ISET),
Interview April 2006
[28]
Anders Andersson, Operation Manager, Vattenfall Gotland,
Interview January 2006.
[29]
Nils-Eric Carlstedt, Swedpower AB,
Interview November 2005
[30]
P.E. Larsson, Manager Product Development and Technical support, SKF
Interview February 2006
[31]
Torben K. Hansen, Project Team Manager, Elsam Engineering Denmark
Interview January 2006
[32]
Hannele Holttinen, Senior Research Scientist VTT,
Interview 2005/2006
[33]
J. Hoflin, Area Export Manager, SPM Instruments AB,
Interview January 2006
69
Reliability performance and maintenance – a survey of failures in wind power systems
Appendix 1 – Incident report from Sweden
Rapportervärd incident
1
(Ifylles en per incident)
Anläggning:
A
1
2
3
Anläggningsnr:
B
Datum när
felet är åtgärdat:
Total hindertid för
denna incident:
1
2
3
4
5
timmar
Beskrivning av incidenten:
Orsak
Väder
extrem vind
is
åska
Utrustning och material
komponentfel / slitage
lösa delar
kontrollsystemet
kortslutning
felkonstruktion
Okänd
Annan (beskrives nedan)
C
D
2
A
1
B
1
2
3
C
1
2
3
4
D
1
2
3
E
1
2
3
4
5
3
A
B
C
Rapporten ifylles, en per incident, när
problemet är åtgärdat och vindkraftverket
snurrar igen. Skickas till: Vindkraftstatistik,
SwedPower AB, Box 527, 16216 Stockholm
70
Åtgärd
Byte av komponent
Justering / rengöring
Annat (beskrives nedan)
6
7
F
1
2
3
G
1
2
3
Berörd del
Rotornav
navkapsel
Rotorblad
bult
skrov
luftbroms
Pitch
mekanisk
elektrisk
hydraulik
lager
Generator
lindningar
motor
lager
El. system
säkring
kontaktor
kabel / kontakt
faskompensering
frekvensomriktare
H
rotorströmsreglering
jordning
Kontrollsystem
kontrolldator
relä
kabel / kontakt
Drivlina
rotorlager
drivaxel
koppling
L
1
2
3
4
5
6
7
I
1
2
3
4
J
1
2
K
1
2
3
4
1
2
3
4
M
1
2
3
N
Givare
vindgivare
vibrationsgivare
temperaturgivare
oljetrycksgivare
effektmätare
varvtalsgivare
kabeltwist
Växellåda
lager
hjul
axel
tätning/smörjning
Mekanisk broms
bromsskiva
bromskloss
Hydraulik
hydraulpump
pumpmotor
ventil
ledning/slang
Girsystem
lager
motor
kugghjul/bana
girbroms
Strukturella delar
fundament
torn
maskinhus
Hela verket
Reliability performance and maintenance – a survey of failures in wind power systems
Appendix 2 - CMS suppliers
Supplier / Name of CMS
SKF / SKF WindCon System
FAG / WiPro
Flender / WinTControl
Certification
Germanischer Lloyd,
Allianz
Germanischer Lloyd,
Allianz
Germanischer Lloyd,
Allianz
Allianz, Gothaer
Allgemeine
Versicherungen AG
Allianz
Gesellschaft fûr
Maschinendiagnose mbH
/Peakanalyzer
Eickhoff Maschienfabrik
GmbH / E-GOMS
Bently Nevada (GE Energy) / Allianz
Trendmaster Pro
µ-Sen Mikrosystemtechnik
Allianz
GmbH / µ-Guard
Brüel & Kjær / Vibro IC
Germanischer Lloyd
Nordex / Condition
Monitoring System
Gram & Juhl
Prüftechnik Condition
Monitoring GmbH /
Vibroweb XP
SPMinstruments
Deutshce Montan
Technologie GmbH (DMT)/
WindSafe
Notes
No installations in Sweden.
Certification is restricted to certain
models of Neg-Micon.
-
Supplies vibration measuring
equipment. CMS have been
installed on Smöla,
Middelgrunden.
Germanischer Lloyd
Allianz
Uses shock pulse measuring. Have
supplied CMS for Slitevind AB,
Gotland
Allianz
71