Damage preventing measures
for wind turbines
Phase 1 – Reliability data
Elforks report 10:68
Fredrik Carlsson, Emil Eriksson, Magnus Dahlberg
August 2010
Damage preventing measures
for wind turbines
Phase 1- Reliability data
Elforks report 10:68
Fredrik Carlsson, Emil Eriksson, Magnus Dahlberg
August 2010
Foreword
The Vindforsk III project V-316, Damage preventing measures for wind
turbines, aims at developing recommendations regarding damage preventing
measures and way of carrying out inspection and condition monitoring to
reduce maintenance cost for wind turbines. The project is carried out by
Inspecta AB and Vattenfall Research & Development.
This report is the reporting from the first phase of the project with an
inventory of existing reliability and failure data in the public sources.
Vindforsk – III is funded by ABB, Arise windpower, AQSystem, E.ON Elnät,
E.ON Vind Sverige, EBL-kompetanse, Falkenberg Energi, Fortum, Fred. Olsen
Renwables, Gothia wind, Göteborg Energi, HS Kraft, Jämtkraft, Karlstads
Energi, Luleå Energi, Mälarenergi, o2 Vindkompaniet, Rabbalshede Kraft,
Skellefteå Kraft, Statkraft, Stena Renewable, Svenska Kraftnät, Tekniska
Verken i Linköping, Triventus, Wallenstam, Varberg Energi, Vattenfall
Vindkraft, Vestas Northern Europe, Öresundskraft and the Swedish Energy
Agency.
Comments on the work and the report have been given by a reference group
with the following members: Jenny Ohlsson from Statkraft, Hans Sollenberg
from O2, and Anders Björck from Elforsk AB.
Stockholm augusti 2010
Anders Björck
Programme maganger Vindforsk-III
Electricity- and heatproduction, Elforsk
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Sammanfattning
Status och kvalitet hos tillgänglig information om tillförlitlighet och skador har
undersökts. Det huvudsakliga målet har varit att utvärdera informationen ur
ett skadeförebyggande perspektiv. Flera offentliga databaser finns, och deras
innehåll har diskuterats i flera artiklar inom litteraturen. Resultatet av
undersökningen kan förefalla vara ganska negativt. Detaljerad information
saknas och rapporteringsdisciplinen försämras på senare år. Information om
hur yttre betingelser som t.ex. lastförhållande påverkar skadestatistiken
saknas helt.
Några komponenter dominerar skadestatistiken. Ett exempel är växellådor där
haveri kan leda till långa stillestånd. Skada på elsystem uppträder oftare men
är å andra sidan lättare att åtgärda. Allvarliga rotorhaverier är ovanliga, men
har inträffat i några fall.
Operatörer
underhåll,
Samarbete
vara viktigt
och försäkringsbolag har en klar målsättning att öka insynen i
insamling av skadedata och skadeförebyggande åtgärder.
med dessa aktörer bör kunna leda till förbättrad kännedom och
i arbetet med skadeförebyggande åtgärder.
I rapporten föreslås förbättringar för framtida insamling av skade- och
tillförlitlighetsdata. En struktur för datainsamling föreslås.
I det fortsatta arbetet kommer skadeförebyggande åtgärder för
vindkraftkomponenter att utvärderas. Detta görs med utgångspunkt från
resultaten i denna rapport. Erfarenheter från andra branscher kommer att
jämföras med nuvarande praxis inom vindkraftbranschen. Dessutom kommer
kunskap om de olika skademekanismerna att användas vid utvärderingen.
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Summary
The state of existing reliability and failure data in the public sources has been
investigated. The prime goal has been to evaluate the data’s usefulness for
developing damage preventing measures. Some publicly available databases
exist, and the data has been presented in several papers in the literature. The
results from the investigation can seem quite negative. Detailed data are
lacking and the level of detailed reporting has even been decreasing in recent
years. Information on the impact of load condition on failures, which is an
important question, are lacking throughout in the statistics.
Some components dominate the failure statistics. These are for example the
gearboxes, where failures lead to long down times. Failures of the electrical
system lead to considerably shorter down times but the failure rate is much
higher. Severe rotor failures seem to be rare, but they occur and the
consequences can be dramatic.
Operators and insurance companies are demanding improved insight in
damage collection, maintenance and overall damage preventing measures.
Closer cooperation with these parties could be a fruitful way of gathering
more useful data.
Improvements for future databases are suggested. A structure for damage
collection is proposed.
Comparing experience of damage preventing measures from other industries,
knowledge about the nature of the damage mechanism and current practice in
the wind industry will be an important tool in the evaluation of different
damage preventing measures. This will be done in the following phases of this
project.
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Table of Contents
1 Introduction
2 List of definitions
3 3 Available databases concerning availability, reliability and
failure of wind turbine components
4 1.1 1.2 3.1 4 Sources for wind turbine reliability and availability data ........................ 4 3.1.1 Vindstat – Swedish Database ................................................. 4 3.1.2 WindStats Newsletter – International database ......................... 8 3.1.3 Fraunhofer IWES ................................................................ 11 3.1.4 VTT .................................................................................. 13 3.1.5 LWK ................................................................................. 13 Available reliability data and data quality
4.1 4.2 4.3 4.4 1 Reference group .............................................................................. 2 Acknowledgements .......................................................................... 2 15 Overall information about wind power reliability ................................. 15 4.1.1 General information ............................................................ 15 4.1.2 German information from WMEP ........................................... 16 4.1.3 German information from WindStats ..................................... 19 4.1.4 Finnish information from VTT ............................................... 21 Detailed failure information for turbine components ............................ 23 4.2.1 Electrical system ................................................................ 23 4.2.2 Generator ......................................................................... 24 4.2.3 Gearbox ............................................................................ 24 4.2.4 Pitch regulation .................................................................. 26 4.2.5 Mechanical brake ............................................................... 26 4.2.6 Hydraulic system................................................................ 27 4.2.7 Yaw system ....................................................................... 27 4.2.8 Sensors and control system ................................................. 27 4.2.9 Rotor and structure ............................................................ 27 4.2.10 Transformers ..................................................................... 29 Experiences from operators and insurance companies ......................... 29 4.3.1 Rotor ................................................................................ 29 4.3.2 Gearbox ............................................................................ 30 4.3.3 General experiences and opinions ......................................... 31 General conclusions of available reliability data .................................. 31 5 Conclusions and recommendations
33 6 References
35 Appendix A. Data provided by Trygg Hansa.
37 Appendix B. Suggestion for a database structure.
40 ELFORSK
1
Introduction
This report presents results from phase one of the project Damage preventing measures for
wind turbines. This title (somewhat differing from the original) was set during the course of
the project to accurately describe the aim of the project. The project aims at evaluating and
proposing damage preventing measures for wind turbines. It is important that the measures
are practical and possible to implement at the current state of technology. Previous
experience of damages and reliability data is an essential input. Introducing damage
preventing methods that require reliability data on a level that does not exist makes little
sense. It is therefore important to define the current level of reliability data. This phase one
of the project, Reliability Data, deals with the investigation of sources of reliability data.
Publicly available databases and the open literature constitute the main sources. These data
are also complemented by interviews and other contribution from operators and insurance
companies. The quality of these data and the usefulness for establishing damage preventing
methods are evaluated.
The initially formulated goals for phase one read:
To analyze how damages depend on important parameters such as age and type of turbine
and to propose a common, standardized procedure for the reporting of damages to a
database.
The question about database procedures is dealt with in more detail in a neighbouring
VindForsk project, “V-344 RAMS-database for wind turbines”. Hence, the project rather
aims at proposing a component breakdown structure suitable to connect with appropriate
damage preventing measures.
In more detail, the following goals have been governing for phase one:
•
Investigating the current state of reliability and failure data in the public sources
•
Evaluating the usefulness and limitations for establishing damage preventing
measures
•
Investigating alternative sources such as direct communication with operators and
insurance companies
•
Investigating how damages depend on important parameters such as age and size.
•
Defining the needs for improvements.
•
Proposing a component structure for databases. The structure should enable linking
between component and damage preventing measure.
Moreover, a structure for the future collection of damage data is proposed. The level of
detail should be sufficient for establishing suitable damage preventing measures. The
structure is intended to be used for the integration with other databases or as a stand alone
database.
This report of phase one will later on be integrated in the final report of the project.
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Figure 1: Modern wind energy plant in rural scenery.
1.1
Reference group
Vindforsk finances this project and the members of the reference group are:
•
Hans Sollenberg, O2
•
Jenny Olsson, Statkraft
•
Anders Björck, Elforsk
1.2
Acknowledgements
The reference group are thanked for valuable comments on the report as well as good
suggestions during the work. Contributing operators and insurance companies are also
thankfully acknowledged.
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2
List of definitions
The below list defines some of the terms used in the report, thus facilitating the reading.
The definitions, where applicable, in all essential follow those in European Standard EN SS
13306[25]. In cases mathematical definitions are needed, these will be given in the text.
Reliability: Ability of an item to perform a required function (under given conditions for a
given time interval)
Availability: 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).
Down time (stop time): Time (real or reported) interval during which an item is in a down
state
Failure rate/frequency: Number of failures of an item in a given time interval divided by the
time interval
Damage mechanism: The physical process causing degradation and potential failure if the
component.
Failure data: information about rate, cause, damage mechanism etc.
Time for replacement: Total time from failure to installation of a new component including
administration, purchase, repair etc.
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3
Available databases concerning
availability, reliability and failure of wind
turbine components
3.1
Sources for wind turbine reliability and availability data
3.1.1 Vindstat – Swedish Database
Vindstat (website www.vindstat.nu) is a Swedish database with operational data from about
800 of the approximately 1300 wind turbines installed in Sweden. The Vindstat database [1]
is operated by Vattenfall Power Consultant AB (VPC) and paid for by the Swedish Energy
Agency. The data collection started in 1988. The information from each wind turbine was
sent by fax and then manually put in to the database. A system for automatic reading of the
turbines once every 24th hour, was installed in 2002. The data consists of total production
during the last 24 hours and current operation mode (if the turbine generator is connected
to the grid or not). This information is stored in a database. The data are presented in
monthly and annual reports.
General information in the database is manufacturer, installation year, and rated power of
each turbine. The total production and the total number of hours the generator has been
connected to the grid can be found. The number of stops is not reported to the database.
This information could possibly be locally available in the logbooks of the individual turbines.
During the period of manual reporting, the reason for a stop was occasionally reported.
Such information could be if the turbine stopped due to grid failure, planned service or
component failure. Sometimes these reasons are still reported to VPC even though the
general system of reporting detailed data was abandoned in 2005. The trend today is that a
few wind power operators own the majority of all recently installed wind turbines. The
service is often performed by the turbine manufacturer during the first years. Thus the wind
power owner often has limited access to detailed reliability data and failure data (Carlstedt
[8]). Therefore, normally only the (total) down time is reported to the database today.
Data from Vindstat [1] is presented in Figure 2 to Figure 7.
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Size of total numbers of installed Swedish wind turbines 1990-2009
<500 kW
500-1000 kW
1000-2000 kW
>2000 kW
2000
2003
100%
90%
80%
70%
Percent
60%
50%
40%
30%
20%
10%
0%
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2001
2002
2004
2005
2006
2007
2008
2009
Year
Figure 2. Accumulated number of Swedish wind turbines divided by power classes from
1990 to 2009. Note: These are only the turbines reporting to Vindstat and not all Swedish
turbines [1].
Share of annual installed Swedish wind turbines 1990-2009, divided by power class
<500 kW
500-1000 kW
1000-2000 kW
>2000 kW
2000
2003
100%
90%
80%
70%
Percent
60%
50%
40%
30%
20%
10%
0%
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2001
2002
2004
2005
2006
2007
2008
2009
Year
Figure 3. Annual share of power classes for installed wind turbines in Sweden. Note: These
are only the turbines reporting to Vindstat and not all Swedish turbines [1].
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The Vindstat reports show the availability in per cent per year of each turbine. The
availability during a specified period is defined as
Availability =
Time of the period − Stoptime
⋅100% ,
Time of the period
(1)
The down (stop) time is the total number of hours that the generator has been disconnected
from the grid (Carlstedt [3]). The time for one year is set to 8760 h.
Since mainly the general stop time is reported to the database today, it is not possible to
extract reliability data on component level (Carlstedt [4]).
The availability of the around 800 Swedish turbines reporting to Vindstat is included in
shown in Figure 4. The mean value is 96% with a standard deviation of 8%. Actually, over
70% have availability over 97%. Furthermore, it can be recognised from Figure 4 that the
mean availability does not really change over time, even though the data show noticeable
differences between individual years.
Figure 8 show that a number of turbines are reported to have 100% availability. This figure
may not be realistic. This leads to questions about the quality of the data and it is possible
that the reported availability overestimates the real data. Hence, availability data should be
analysed with caution. Availability of 100% is reported in cases of absence of information
[4]. Since some of the turbines reporting to Vindstat actually fail to report they will increase
the Availability to un-realistic levels. It is suggested that this problem is further scrutinized
and corrected in future studies. According to information, IEC is developing a standard for
availability which may be a solution to the problem.
100
90
80
Availibility [%]
70
60
Wind mill
50
Mean
40
30
20
10
0
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Installation year
Figure 4. Availability in 2009 of Swedish turbines included in the Vindstat database [1] by
installation year.
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300
Number of turbines
250
200
150
100
50
0
<88
89
90
91
92
93
94
95
96
97
98
99
100
Availibility [%]
Figure 5. Histogram of wind turbine availability in Sweden during 2009 from Vindstat [1]. A
significant number of the turbines show an availability of 100%, which is caused by
inadequate reporting routines.
The availability for different turbine sizes is shown in Figure 6. The availability seams to be
independent of size. Most turbines have a reported availability well over 90%. Only three
power classes have a lower availability. Furthermore, the availability for different
manufacturers for turbines > 1.5 MW have been investigated in Figure 7. The difference is
not significant between the manufacturers. Siemens has the highest availability for the year
2009. However, once again the availability data should be treated cautiously.
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Availability of Swedish wind turbines divided by power class
100
90
80
Availibility [%]
70
60
50
40
30
20
10
0
150
200
230
450
490
500
600
660
750
800
900
1000
1425
1500
1750
1800
2000 2300
2500 3000
Pow er [kW]
Figure 6. Availability in 2009 divided by power class of Swedish wind turbines according to
Vindstat [1].
100,0
90,0
80,0
70,0
60,0
50,0
97,1
93,2
97,8
94,9
Siemens
Vestas
40,0
30,0
20,0
10,0
0,0
Enercon
NEGMicon
Figure 7. Total availability of Swedish wind turbines larger than 1,5 MW during 2009
according to Vindstat [1].
3.1.2 WindStats Newsletter – International database
WindStats Newsletter is a quarterly international wind energy publication with news,
reviews, and wind turbine production and operating data from turbines in Sweden,
Denmark, Germany and Finland. The data in WindStats are on a similar format as the data
in the Swedish database Vindstat, see chapter 3.1.1. Vindstat constitutes the Swedish input
to WindStats. The information from Germany comes from Betrieber-Databasis/IWET, from
Finland the information comes from VTT Technical Research Centre of Finland and for
Denmark the Danish Energy Agency is responsible [5].
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The total number of down hours for individual turbines in Sweden, Denmark, Finland and
Germany is available in the WindStats Newsletter. The number of stops and down hours is
also presented in more detail for all German turbines. These data can to some extent be
related to the cause of the stop. This is more useful than only overall data. The numbers of
stops and the total down time is reported. The data are also split up on the cause of the
stops (Weather, Grid, etcetera) and which component of the turbine that failed (Gearbox,
Rotor etcetera). The Betrieber-Databasis/IWET has been in use for over 20 years and
around 20000 individual turbines are reporting to the database (Keiler [6]). A table with
original data from WindStats Newsletter for the third quarter of 2009 is presented in Table 1
below. Unfortunately, the failure data are not specified for wind turbine size, type or
manufacturer. Neither is information on turbine age available.
Table 1. Number of stops and down hours for individual turbines in Germany, for the third
quarter of 2009. Around 21000 turbines were installed at the time according to WindStats
[7].
Reliability data from WindStats during 1999-2008, also divided on wind turbine
components, are available thanks to Canter [8]. However, the current reporting routines do
not give as detailed information compared to some years ago. The number of stops reported
under the categories “Entire unit, “Other” and “Only down hours reported”, i.e. nonspecified reason, increases continuously during period 1999 to 2009. Clearly, the
possibilities for adequate analysis of the data are hampered. In 1999, 62% percent of all
stops were reported under these categories. In numbers this means 4700 stops out of
totally 7600 stops, of around 7000 turbines. In 2008, the corresponding figure was 93%
(39500 stops out of totally 42600 stops, and around 20000 turbines). The trend is evident
from Figure 8 and Figure 9. The number of stops reported in detail remains fairly constant
over the period. Thus the number of stops without specification increases in absolute
numbers.
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Total numbers of stops, 1999
Entire unit
Rotor
Air brake
Mech. brake
Pitch adjust.
Main shaft/bearing
Gearbox
Generator
Yaw system
Anamometer
Elec. controls
Elec. system
Hydraulics
Sensors
Other
Only stop hrs. reported
Figure 8. Total number of stops split on components for German wind turbines reporting to
WindStats in 1999 [8]. The total number of stops is around 7600, of which 38% is reported
on individual components. The total number of turbines was around 7000.
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Total numbers of stops, 2008
Entire unit
Rotor
Air brake
Mech. brake
Pitch adjust.
Main shaft/bearing
Gearbox
Generator
Yaw system
Anamometer
Elec. controls
Elec. system
Hydraulics
Sensors
Other
Only stop hrs. reported
Figure 9. Total number of stops split on components for German wind turbines reporting to
WindStats in 2008 [8]. The total number of stops is around 42600, of which only 7% is
reported on individual components. The total number of turbines is around 20000.
3.1.3 Fraunhofer IWES
The German institute Fraunhofer Institut für Windenergie und Energiesystemtechnik
(Fraunhofer IWES, formerly Institut für Solare Energieversorgungstechnik, ISET) has
performed a large monitoring programme for wind power turbines. The monitoring
programme was called Scientific Measurement and Evaluation Programme (WMEP).
Faulstich [9] reports that 1500 turbines of a total rated power of 250 MW were closely
monitored during 1989 to 2006. During this period over 64000 maintenance and repair
reports and 193000 monthly operational reports were analysed. The German Ministry for
the Environment, Nature Conservation and Nuclear Safety (BMU) has supported the German
Wind Monitor project since 2007, which can be seen as a continuation of the WMEP.
Unfortunately, reporting failure statistics was only mandatory during the original time of the
WMEP period. Today the reporting is done on a voluntary basis. Consequently, the number
of wind turbines involved dropped substantially after the last year of the program
(Echiavarra et al [10]). The continuation of the WMEP on the smaller scale however still
releases a report once a year, the Windenergy Report Germany [11].
Unfortunately, no raw data, but only diagrams and graphs were available from WMEP for
this project. However, the available information in Windenergy Report Germany is still of
use. The trend of the annual share of power classes for wind turbines installed each year in
Germany are presented in this report, see Figure 10.
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Figure 10. Annual share of power classes for installed wind turbines in Germany, presented
in Windenergy Report Germany [11].
Another interesting trend is how the availability of wind turbines varies over the years,
which has been studied in Figure 11. Technical availability in Figure 11 is defined as:
Availability =
Available time
⋅100% .
Nominal time
(2)
The Available time is the time when the turbine is functional and available for generation
and Nominal time is the total reporting period. This means that the availability numbers
from WMEP, which are showed in Figure 11, are calculated more or less in the same way as
the availability from Vindstat [3], see 3.1.1.
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Figure 11. Average technical availability for German wind turbines from the Windenergy
Report Germany [11].
The availability levels on average have always been between 98 and 99 % most of the time
since the start of the monitoring programme (Figure 11,). It was slightly below 97.5% in
2002. It is once again noted that these values are very (unrealistically?) high and should be
analysed with caution. The availability and reliability data from the Windenergy Report
Germany 2008 is further analysed in chapter 4.
3.1.4 VTT
In the end of 2008, there was 142 MW of installed wind power in Finland The aim of the
Finnish government is to increase to 2000 MW in 2020. Every month since 1996, Finnish
wind power operators, manufacturers and third parties send in production and down time
data of wind turbines to VTT. Detailed information about down time is sent manually and on
a voluntary basis. VTT then compile the information and publish it in monthly and annual
reports, similar to WindStats Newsletter and the Swedish database Vindstat. These reports
include energy production and total down time for each turbine, while details about failures
on component level are classified information and are therefore not published in detail
(Stenberg [12]).
The manual system for reporting details about wind turbine failures in Finland is similar to
the system that was used up to 2005 in Sweden (chapter 3.1.1.) The reported down times
are split up between icing, service, grid etcetera. This is further discussed in chapter 4.
3.1.5 LWK
The Landwirtschaftskammer in Schleswig-Holstein has collected data from some hundred
wind turbines in the Schleswig-Holstein federal state of Germany. The reliability data from
LWK are categorised on turbine size and wind turbine concept. The data from 1994-2004
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are analysed in-depth in a PhD thesis by Spinato [13], where turbine component reliability
data were divided in three power classes; around 300 kW, around 500 kW and >1 MW. The
turbines in each group were also categorised by model and technology (like direct or
indirect drive, use of converter, and pitch or stall regulation).
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4
Available reliability data and data quality
4.1
Overall information about wind power reliability
The quality and usefulness of the information about wind turbine components in the
databases (chapter 3) are discussed in this chapter. These discussions are complemented
by experience from wind power operators and insurance companies. Information from
individual papers and reports will also be discussed.
4.1.1 General information
When addressing reliability of components, it is customary to identify three different stages
of failure rates. The first stage is ‘Early failures’, or ‘Infant Mortality’, occurring in the
beginning of operation. This phase is generally followed by a longer period of ‘random
failures’, or “Normal Life’, with a statistically constant failure rate. Finally, the failure rate
due to wear and damage accumulation (‘wear-out failures’) increases with operational age.
The total life period and the individual phases are normally rather easily identified, and can
be illustrated with the so-called Bathtub Curve, see Figure 12. Do wind turbine components
follow this curve? Unfortunately for wind turbines, there are probably not enough data
available to identify these stages (Echiavarra [10]).
Figure 12. The Bathtub Curve [14].
It is of interest to define different parameters for which to group the reliability data. Ideally,
it should be possible to study the reliability data as a function of each parameter. Such
parameters are manufacturer, turbine type, turbine size, turbine age and also IEC wind
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turbine class. This project partly aims investigating if it is possible analyse the reliability
data as functions of these parameters.
4.1.2 German information from WMEP
An overview of failure causes for the turbines examined in the WMEP study is illustrated in
Figure 13 and presented in [11]. These figures are based on failures reported from around
1500 German turbines during 2008. The majority of failures are due to component failure or
to malfunction of the turbine control systems. In 19% of the cases, the faults were caused
by external influences such as storms, lightning, ice accretion or grid outages.
Figure 13. Frequency of failure causes on German wind turbines, with a total of 32166
failures presented by Faulstich et al [11]. These figures are based on failures reported
during 2008.
The distribution of failed components of the turbines in WMEP during 2008 is shown in
Figure 14. This information is divided on power classes. It is interesting to see that the
number of failures in the electrical system seems to increase dramatically with turbine size.
One potential reason could be larger turbines usually being newer and designed with
somewhat immature technology compared to the older and smaller turbines. The larger and
newer turbines probably use more electrical components, often with higher complexity. This
implies that more components can fail (Spinato [13]).
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Figure 14. Affected components in Germany, divided by power class, Faulstich et al [11].
These figures are based on failures reported during 2008.
Figure 15 shows failure frequencies and down times (stop times) per failure for different
components. It is apparent that failures on the electrical system (excluding the generator)
occur frequently. On the other hand, these failures could be handled relatively quickly. This
condition is likely to apply especially for larger turbines, with a higher failure rate of
electrical components. It is also probably easier to find spare parts for these newer turbines,
which enables quick fix. Failures occur more seldom for the generator and the drive train.
However, these are larger components, and the repair time increases. The time for repair of
the gearbox on a large turbine, for example, is rather low in Figure 15. It should be noted,
however, that there are only a few large turbines included in the study, which may bias the
results. Hence, the figures for the large turbine should be interpreted with caution. Thus
there is a possibility that no serious failures, demanding time-consuming repairs or
replacement, occurred during the period. This scenario could lead to a considerable
underestimation of the average down time at gearbox failure.
Figure 15 also indicate that larger wind turbines have a higher annual failure rate than
smaller turbines. The down time is however generally much shorter than in the case of
smaller turbines. According to Faulstich et al [11], this is however more likely the result of
quick technical support rather than due to high reliability of the larger turbines.
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Figure 15. Frequency of failures for different subassemblies and typical down time per
failure for different power classes, presented in Faulstich et al [11]. Data from the around
1500 German turbines included in the WMEP from 2008 [11]. (Note: units for P in kW).
Annual downtime per subassembly
Annual downtime [days]
P<500 kW,
500 kW <P<1000 kW,
P>1000 kW
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pa
rt
Dr
ive
tra
in
1,8
1,6
1,4
1,2
1
0,8
0,6
0,4
0,2
0
Subcomponent
Figure 16. Total annual down time per subassembly, derived with data from Figure 15 [9].
Data from the around 1500 German turbines included in WMEP during 2008 [11].
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Figure 16 indicates that the electrical system and the electronic control system cause the
longest down times. The information in Figure 16 is obtained by multiplying the failure rate
with the down time from Figure 15. On this ground it could be argued that electrical
components should be prioritised in terms of damage preventing measures.
In contrast, the larger mechanical components with lower annual failure rate dominate for a
single failure. This makes these components more sensitive to individual failures. A few
additional failures could dramatically increase the total down time. This is especially true for
the gearbox. This sensitivity indicates a high need for damage preventing measures.
In the end the priority for damage preventing measures will be subjective. It is clear from
the statistics that the type of damage preventing measure may be very different between
the components.
4.1.3 German information from WindStats
Data from WindStats during 1999-2008 is shown in Figure 17 to Figure 20. Today, around
20000 German turbines report to WindStats (whereof some, but not all, also report to
WMEP). It is important to note that the failure classes “Entire Unit”, “Other” and “Only down
hours reported” were excluded from the data in these four figures. This is done in order to
compare only failures with detailed information. As discussed earlier in this report, 62% of
all reported failures were reported without detail in 1999 and as much as 93% of all failures
in 2008. However, assuming that failures without detailed information would not bias the
data, the proposed presentation gives a clearer picture of the distribution of failure between
components. It should be remembered that the data maybe somewhat misleading since
such a small fraction of downs are reported with details. Only a more thorough study with
more data available could confirm if the distribution is valid for the entire population.
Figure 17. Total number of stops between 1999-2008, based on stops reported in some
detail. Hence, failures reported under the growing subclasses “Entire Unit”, “Other” and
“Only down hours reported” are excluded. Based on data from WindStats and Canter [8].
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Percent of all stops
Percentage of total stops,
divided on turbine components
100%
80%
60%
40%
20%
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
19
99
0%
Year
Sensors
Hydraulics
Elec. system
Elec. controls
Anamometer
Yaw system
Generator
Gearbox
Main shaft/bearing
Pitch adjust.
Mech. brake
Rotor
Figure 18. Percentage of total stops between 1999-2008, based on stops reported in some
detail. Hence, failures reported under the growing subclasses “Entire Unit”, “Other” and
“Only down hours reported” are excluded. Based on data from WindStats and Canter [8].
Total down time
divided on turbine components
Sensors
Hydraulics
Hours [h]
Elec. system
200000
Elec. controls
150000
Anamometer
Yaw system
100000
Generator
50000
Gearbox
0
Main shaft/bearing
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Pitch adjust.
Year
Mech. brake
Rotor
Figure 19. Total number of down time between 1999-2008, based on stops reported in
some detail. Hence, failures reported under the growing subclasses “Entire Unit”, “Other”
and “Only down hours reported” are excluded. Based on data from WindStats and Canter
[8].
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Sensors
Hydraulics
Elec. system
100%
Elec. controls
80%
Anamometer
60%
Yaw system
40%
Generator
20%
Gearbox
Main shaft/bearing
0%
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
Percentage of total down time
Percentage of total down time,
divided on turbine components
Year
Pitch adjust.
Mech. brake
Rotor
Figure 20. Percentage of total down time between 1999-2008, based on stops reported in
some detail. Hence, failures reported under the growing subclasses “Entire Unit”, “Other”
and “Only down hours reported” are excluded. Based on data from WindStats and Canter
[8].
Figure 17 and Figure 18 shows that the electrical system and electrical controls contributes
significantly to the number of stops. Furthermore Figure 19 and Figure 20 show that the
electrical system and the electrical controls also contribute significantly to the total down
time. The generator and, above all, the gearbox contribute significantly to the total down
time. These components fail comparatively seldom in comparison to the electrical system,
but each failure leads to individually long down times. Rotor failures also seem to contribute
to the total down time to the order of 10%. This may seem somewhat surprisingly high.
4.1.4 Finnish information from VTT
The quality and continuity of the failure reports in Finland vary due to the manual reporting
system. The documentation is often incomplete. The results from Stenberg [15] should
hence be regarded as trends. The results should not be used for direct comparison with
other statistics. A substantial amount of data comes from relatively new turbines. This
means that they might suffer from failures due to immature technology. Those turbines
were however not included in Stenberg’s report [15]. Some turbines also suffered from bad
reporting routines, which excluded them from the report as well. In the end, a total of 72
wind turbines with a total rated power of 73 MW were examined. A total of 37 of these
turbines were of size 1 MW or larger. Because of the limited number of turbines, all data are
analyzed in one single group. The failure data in the Finnish report are collected during the
period 1996-2008.
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Total number of stops per component
Rotor
Brakes
Pitch adjust.
Gearbox
Generator
Yaw system
Elec. controls
Elec system
Hydraulics
Sensors
Structure
Heating
Grid connection
Other
Only stop hrs reported
Figure 21. Total number of stops on Finnish turbines, reported to VTT during 1996-2008.
(Stenberg [15]).
Total stop time per component
Rotor
Brakes
Pitch adjust.
Gearbox
Generator
Yaw system
Elec. controls
Elec system
Hydraulics
Sensors
Structure
Heating
Grid connection
Other
Only stop hrs reported
Figure 22. Total down time (stop time) for Finnish turbines reported to VTT during 19962008 and presented in Stenberg [15].
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4.2
Detailed failure information for turbine components
The following chapters deal with the reliability and failure data in view of the different
components of a wind turbine. Figure 23 includes a sketch to provide an overview of the
components in order to facilitate the reading of the discussion.
Figure 23. Overview of components in a wind turbine.
4.2.1 Electrical system
As can be seen in Figure 14 and Figure 15, the numbers of failures in the electrical systems
are high, but the down time is often low. Fuses, short-circuited cables or defect components
are reported to be the most frequent failures. Many of these components are relatively
common, cheap and small, which make it possible to have them in stock or delivered within
a short time.
Echiavarra et al [10] claimed in 2007 that there are more failures in the electrical systems
for synchronous generators than for induction generators. Furthermore, their data indicate
that wind turbines with synchronous generators use a full power converter, which is
regarded as a new component possibly representing a less mature technology. It is claimed
that this leads to an increase of failures compared to the more mature technology with
induction generators. Tavner et al [16], claim that the reliability of wind turbine converters
is considerably worse than the reliability of converters in other industries. Due to time
constraints, this argument was not further investigated deeper during this project.
Spinato [13] also reports data where the number of failures in the electrical system is
dominant. Even if these failures might not take long time to repair, a high number of
failures can lead to long total down time. It is therefore important to have quick access to
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spare parts and service personnel. It is also important to have quick access to the turbine
site. The latter might be harder to fulfil at an offshore wind farm.
According to Faulstich et al [18], failures of electrical subassemblies may occur at a variety
of parts in different locations. The failure cause may also be different. This may make failure
prediction and damage prevention more difficult.
Fires in wind turbines are often caused by failures in the power electronics and short-circuit
in the electrical system (Ny Teknik [17]).
4.2.2 Generator
The study of Stenberg [15], of maintenance and replacement data points out failure of the
generator slip rings as the most common failure mode of a generator. The shortest time for
replacing a generator was 14 hours. The longest time to get the turbine in operation again
after a generator failure was 1306 hours. Note that these figure include the total time from
failure to fulfilled replacement. Hence purchase, administration, installation controls etc may
be a substantial part of the time. Average time for a replacement was 410 hours and the
replacements are almost evenly spread out over the age of the turbines.
As mentioned in chapter 4.2.1, Echiavarra et al [10] have shown that induction generators
are generally twice as reliable as synchronous generators. A possible reason is that the
synchronous generators use full power converters. These were quite new components in
wind turbines at the time. The WMEP database (chapter 3.1.3) was used as input to
Echiavarra et al [10]. A total of 83% of the included turbines used an induction generator.
Many of the turbines using synchronous generators in this study were of relatively new
designs.
According to Tavner et al [16], the reliability of wind turbine generators in general is worse
than the reliability of generators in other applications. No discussions about the reason for
this have been found.
4.2.3 Gearbox
The gearbox is one of the components that cause the longest down time. The reason for this
is of course the long time for repairing or replacing a gearbox. Also, the failure rate is far
from negligible leading to considerable total down times.
Ribrant and Bertling [19] focus on failures of Swedish wind power plants during 1997–2005.
The gearbox is the most critical component in a wind turbine when it comes to down time
per failure. Table 2, shows failed subcomponents in a gearbox for turbines with a rated
power of 490 kW or more.
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Table 2. Gearbox failures during 1997-2004 on Swedish wind turbines larger than 490 kW,
as reported by Ribrant [19]. The number of failures caused by wear is shown in the two last
columns and this cause of failure is called B1.
The data in Table 2 were calculated from information given from Vindstat [1], and this data
was reported manually to Vindstat. Automatic reporting routines were already in use at the
time when [19] was written, but detailed information from some turbines were still reported
manually. Each type of failure has a specific code assigned. The letter “I” stands for
gearbox, and the subsequent number is the code indicating which part of the gearbox that
failed. Table 2 also shows unspecified failures. According to Ribrant and Bertling it was
possible to examine additional text added in the database of failures. This text revealed that
about half of the unspecified gear failures (I-other) correspond to serious failures that
resulted in a replacement of the entire gearbox. It was also revealed that most of the
bearing failures also demanded a replacement of either the gearbox or the gearbox
bearings. B1-failures, which can be seen in the title of the last two columns, are failures
caused by wear. As can be seen in Table 2, the majority of gearbox failures are caused by
wear, which mean long time damage mechanisms such as fatigue or mechanical abrasion.
Stenberg [15] also reports that the gearbox, with all its sub-components, is the equipment
that has caused the longest down times. There are ten gearbox replacements reported in
the Finnish data, during a total operational time of 522 years. Surprisingly, the shortest
time for replacing a gearbox was just 24 hours. The gearbox in this particular case was
actually replaced before it actually failed, which is important information. However, no
information about the detection method, CMS monitoring system, inspection etc., was
given. This indicates the benefits of being able to predict failures and being able to replace
the component before failure. This could save considerable time and money. The average
total time for replacing a failed gearbox with a new one was 622 hours, while the longest
stop lasted for 2245 hours. Of course the delivery time and other factors like availability of
cranes affect the total time for replacing a gearbox. During the period of collecting
information, nine gearboxes with a repair time over one month were reported.
The gearbox of a conventional turbine is a source of noise often complained about. This will
not be a problem with direct drive machines. Of course, the direct drive concept also
eliminates the problems with long down times. However, the direct drive concept may suffer
from other problems that have yet to appear with the increased number of direct drive
turbines. A possible source for failure is the multi-pole generator and the full size converter
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that is needed (Spinato [13]). However, Faulstich et al [22] reports that higher failure rates
for these components does not automatically implicit lower availability. Despite a relative
high failure rate, the annual down time seem to be low compared to turbines with
gearboxes. Tavner et al [16], state that the total availability of a direct drive wind turbine is
higher even though the failure rate of generators and converters is higher than the
corresponding failure rate of gearboxes, generators and converters in geared wind turbines.
Faulstich et al [22] suggest that the differences in failure rates for direct and indirect drive
machines could be due to:
–
Much larger number of coils used in direct drive machines. The failure rate could
perhaps also be improved by replacing the field coils by permanent magnets.
–
The larger diameter of the direct drive generator makes it more difficult to protect
the windings from the environment, like humidity and contaminations.
–
Insufficient standardization of the manufacturing process, as a consequence of
relatively small production series
According to Tavner et al [16], gearbox technology should be regarded as a mature
technology. Therefore, the authors claim that substantial improvement of the reliability of
wind turbine gearboxes is unlikely in the future without radical redesign of the gearbox. This
conclusion can seem to be rather pessimistic in a view of damage preventing measures. It
also highlights the necessity of further research in the area of gearbox in wind turbine
applications. Possible differences against other applications should be investigated. The
difference compared to other technologies should be addressed. It should be of strong
interest to more closely address the difference between gearboxes in wind turbine
applications as compared to other applications. The difference could potentially lie in the
design, in the load conditions, in the maintenance etc. The data in this study clearly indicate
the bearings as the most failed parts. It remains yet to identify and distinguish the
particular conditions for bearings in wind turbine applications. Such factors may be load
conditions, lubrication, contact pressure, rotational speed etc.
4.2.4 Pitch regulation
A total of 51 turbines in Stenberg [15] are reported to have failed. The failure occurred in
the blade angle regulation. A total of 16 were stall regulated. This technology is out-of-date
today. The most common failure for the pitch regulation reported to VTT is failure in the
hydraulic system. Problems with the motor bearings and the measurement equipment are
also reported. The total down time was of some tens of hours in average per year. An
interesting observation is that increased failure frequency for the pitch regulation has been
found at a site for testing equipment in cold climate.
4.2.5 Mechanical brake
The mechanical brakes are worn out during normal operation of wind turbines. Adjustment
and replacement of the brakes are recurrent events. These activities are not very time
consuming. However, an event with a down time of around 90 hours was reported to
Stenberg [15]. This event was related to the hydraulic system of the brake.
Air brakes are not included in this report, since modern turbines use pitch and not stall
regulation. Air brakes are only used on stall-regulated turbines.
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4.2.6 Hydraulic system
Hydraulic problems are recurrent events and constitute one of the most common reasons
for technical problems in a turbine (Stenberg [15]). Replacements of hydraulic pumps and
tubes are common and occur in more or less all turbines. The approximated downtime for a
failure in the hydraulic system is in most cases around 100 hours, but in extreme situations
the downtime has been over 300 hours.
4.2.7 Yaw system
Problems with the yaw system are also rather common and the most common solution is
replacement of the yaw motor, see Stenberg [15]. The reason for problems is for example
icing. In some cases there is a long waiting time for a new motor. The approximated
downtime for a failure in the yaw system is in most cases below 20 hours, but in the worst
cases the downtime has been around 45 hours.
4.2.8 Sensors and control system
There are many different sensors in a turbine and problems occur often. The down time is
normally around 20 hours since only a single sensor replacement is required. In cases of
older turbines, the needed sensors might not be in production anymore. Hence, the down
time can increase dramatically (Stenberg [15]) in these cases.
Stenberg [15] further concludes that the most common failures of the control systems lead
to replacement of the electronic circuits. This is easily done if new circuits are readily
available. However, for some advanced failures, the down time can be extended. According
to Faulstich [18], 75% of all failures of the control system are caused by hardware problems
on the control unit. Software and communication problems are also rather common.
4.2.9 Rotor and structure
Problems with the rotor are unusual according to Stenberg [15]. However, in WindStats the
failure rate for the rotor is not negligible and stands for around 10% of the total stops and
also around 10% of the total downtime for a turbine, see Figure 18. The downtime can be
over 70 hours for a single failure . This holds especially if the rotor must be dismounted
from the turbine. This may happen if the main shaft or bearing, or a rotor blade must be
replaced. Strokes of lightning for example, can require blade replacement.
Problems with the structure (tower, for example) are rare, but the down time can be
approximately 100 hours, according to Stenberg [15].
Blades have come off at some recent incidents reported in media. Such failures occurred in
Falkenberg in 2009 and in Norway, at Hundhammerfjell 2009. The Falkenberg failure was
reportedly due to the lack of tightening of the bolts. A damage investigation report of this
failure is about to be released (according to information), however still not available.
Further, the complete rotor came off in an accident in Jylland, Denmark in 2009. This
happened due to complete failure of the main shaft. In January 2008, a 850 kW turbine in
Gotland lost one rotor blade. Media reported that the failure was also in this case due to
lack of tightening of the bolts. It is yet to investigate why these bolt failures could happen.
Tightening control is a rather easy operation. Preventing these dramatic types of failures will
be addressed in the coming phases of the project.
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Figure 24. Falkenberg rotor failure in 2009 (Ny Teknik).
Figure 25. Rotor failure due to broken main shaft in Jylland 2009. Fracture surface
(Ingeniøren, Danish journal)
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4.2.10
Transformers
According to Stenberg [15], a component that contributes to long down times is the
transformer. This fact is confirmed in He [20], in which reliability data for equipment in the
distribution network were studied. No specific reasons are mentioned for transformer
failures and the transformer down times are not reported explicitly either. However, a
transformer that caught fire in Finland caused a stop for 1159 hours. Most transformer
failures are related to breakers and disconnectors.
4.3
Experiences from operators and insurance companies
Two operators, operator A and B, have contributed with their experience in communication
with the project. Both operators have experience of damages on the equipment, primarily
on the gearbox. One of the operators claimed that the openness with regards to problems
and maintenance is currently increasing in the industry. This should lead towards increasing
reliability. Due to the rapid development, the industry is still immature. It is clear that
monitoring systems will be more common in the future. Alongside, the number of
inspections will probably increase. Today, some insurance companies have mandatory
inspections connected to the insurance. Both operators agree that there is an immense
value in being able to predict failures in order to have planned replacements before failure.
This is clearly supportive of the findings in 4.2.3. One of the operators is now introducing a
maintenance database. So far, the database contains very limited data. A general problem
is that the manufacturers are performing the maintenance during the warranty time. Thus
the operators have limited insight during the early operation. However, the database is
promising and in the long run serve to provide valuable information for improved
maintenance.
The operators interviewed have not experienced generator or rotor failures to the extent
reported in the literature. The gearbox is the main cause of problems. A crane is needed for
dismounting the gearbox from the turbine. Long repair times also affect the energy
production and lead to lower incomes. There are also some components with reoccurring
problems that cause long total down times due to the high failure frequency. The power
electronic converters used for the variable speed function are such components according to
Operator A. This component is rather easy to repair, even if there is a certain cost to take
into consideration for repairing this component repeatedly.
Generator problems with long down times are not as common as in the statistics, see
chapter 3. The most common failure on a generator is, according to Operator A, related to
the bearings. This problem can be fixed directly on site and without long down times.
Operator B is of a similar opinion. The generator is rather reliable but dependent on
maintenance programs such as exchange of slip rings.
The insurance company Trygg-Hansa has provided a list of failures for the project. However,
the list confirms the importance of the gearbox in the failure data. See appendix A.
4.3.1 Rotor
Problems with the rotor are claimed by operator A not to be common. Most problems occur
due to a manufacturing defect in the laminate. Operator B also mentions lightning strikes as
a cause of rotor damage. Visual inspections of the blades are always performed at
inspections, but in some cases the Eigen frequency of the blades is also measured. Blade
defects can in this way be found at an early stage by noticing a change in the measured
Eigen frequency. A majority of all blades are made of epoxy. Some blades are made of
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carbon fibre for a reduced weight. However, blades made of carbon fibres are stiffer than
epoxy blades and also conduct electricity. It is desirable with some damping in a blade and
a conductive blade can easily be ruined in case of a stroke of lightning. Both these
properties of a carbon fibre blade are undesirable. The risk for a blade to come off at the
bolts is minimal according to both operators. The bolts are tightened during inspections.
4.3.2 Gearbox
Both operators agreed that the gearbox is a component that strongly contributes to the
total down time of a turbine. It is the experience that the gearboxes have a life expectancy
of 6-8 years. Gearbox bearings are the most critical and life-limiting components. The high
speed and the intermediate shaft bearing are critical parts of the gearbox. Failures in these
parts of the gearbox can possibly be repaired at the site, in the best case without removal
of the gearbox. Failures on the low speed shaft are even worse, since this type of failure
demands an expensive lift. Repairing these failures offshore means even higher costs.
There are also some fatigue related failures in gearbox teeth. The operator A argues that
these failures are due to material imperfections, stemming from the hardening of the
material at manufacturing.
Operator A claims that the fact that a wind turbine gearbox is driven on the low speed side,
makes it more vulnerable than gearboxes in other applications. Normally, the motor is
connected to the high speed side. This statement remains to be investigated.
CMS/Vibration sensors are often used on gearboxes in newer turbines for monitoring
purposes. However, these detectors are hard to place on the rotating planetary gear wheels
of the gearbox, which is one of the parts that suffer from the highest amount of failures.
Figure 26. Sketch of a gearbox typical for wind turbines, here a one stage planetary and
two spur stages.
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4.3.3 General experiences and opinions
Smaller wind turbines around 15 years of age are relatively simple compared to today’s
turbines of several MW. Operator A claims that these smaller turbines (around 500 kW) do
not have the many problems associated with turbines manufactured around 6-7 years ago,
even if they are about to be worn out after many years of operation. One reason for this
could be that smaller turbines were more robustly dimensioned. Larger turbines (>2 MW)
were under development some years ago. Today, these turbines have a total gear ratio of
around 100, which is very high. This should be compared to older, smaller turbines with
gear ratio at 30. Difficulties to handle the high gear ratio can be one participating factor in
the gearbox problems.
The reason for the high gear ratio for larger turbines is that the blade tip speed and the
generator rotational speed (about 1500 rpm) are fairly constant irrespective of turbine size.
Hence the rotational speed of the low-speed shaft is lower for turbines with large rotors,
requiring a higher gear ratio to obtain the desired generator rotational speed.
The manufacturers competed against each other in developing larger turbines 6-7 years
ago. New models were constantly developed and produced in short series. The
manufacturers did not prioritize the development and improvement of existing models. The
current models have had more time to develop and mature. However, problems with the
gearbox for example, are still a relatively big. An operator can handle a single replacement
of the gearbox during the turbines expected life but a second or third major repair could
potentially ruin the economy.
The turbine manufacturer is normally responsible for the turbine service during the warranty
time, and also some time after the warranty has expired. A normal time for such a service
contract is 2-5 years. The operator does not have a full insight in the service activities
during this time. This applies especially for the cost of the service and repair activities.
Limited influence on the total cost of maintenance results (operator B).
Operator A is convinced that it would be valuable to have an efficient monitoring system
that could signal if components (like gearboxes) are about to fail. To replace damaged
components before they fail could save considerable time and cost. Operator B regards CMS
as necessary. But operator B also believes that inspection programs are important steps
towards successful investment of wind power turbines.
4.4
General conclusions of available reliability data
There are just a few reliability databases for wind turbine components. Almost all papers
referenced in this project are based on the same databases. The databases all have severe
limitations. Such limitation is the lack of detail. Failure data on component level are very
rare. A substantial part of the data is on the entire unit. More detailed data mainly come
from older types of turbines.
It would be ideal to group reliability data on important parameters. Such parameters could
be turbine manufacturer, rated power, age and load conditions (for example IEC wind
class). Most of the available data are unfortunately not classified in similar groups. WMEP
has grouped some of the data in turbine power class and turbine location (Coastline, Low
mountains and Inland), but their amount of data is still limited. Most of the available data
are also some years of age, and less information about turbines with a rated power larger
than 1 MW is available.
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The data from VTT show some differences compared to WMEP and WindStats. The
hydraulics contributes more significantly to both the number of stops and the total down
time. Moreover, the failure rate for the electrical system is lower. However, the Finnish data
come from a small population of turbines, which of course affect the validity of the
comparison.
It is indicated from the literature that the gearbox, and in some cases also the generator,
contributes most to the total down time, even if the failure rate is rather low. However, the
Swedish operators that were interviewed within the project did not completely agree. Their
experience is that generators contribute very little to the total down time for their turbines.
The electrical system also contributes significantly to the total down time. This is due to the
high failure rate. On the other hand, the electrical systems are easy to repair. Due to the
easiness of replacement the electrical system failures may be regarded as less severe than
failure of large mechanical components. Almost all data used in this project mainly come
from onshore turbines. Offshore conditions may alter the priority.
An attempt to compare the sources can be found in Table 3 below. The subassemblies with
the highest failure rate and longest down time per failure are presented.
Table 3. Turbine subassemblies with the highest failure rates and longest down times
according to different databases. For WindStats, the first line is for German turbines, the
second line for Danish turbines. [22].
According to Bertling [24], the gearbox, control system and the electric system are the
three sub-systems that have the longest total down times in Sweden (named Elforsk in
Table 3).
There are of course useful data in the various databases studied in this project. However,
improvements are clearly needed if the data should be able to provide a basis for efficient
damage preventing measures.
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5
Conclusions and recommendations
This report has summarised the current state of public reliability databases and their
usefulness for implementing damage preventing measures. It is tempting to be somewhat
pessimistic about the results. The level of detail is far from sufficient and the quality of
reporting is even decreasing. This makes it impossible to make a relevant analysis of the
damage dependency on parameters such as age and size, which was a stated goal of the
project. These limitations must be regarded in the subsequent phases of this project. To
balance these negative results, it is encouraging to note that operators and insurance
companies are demanding improved insight in damage collection, maintenance and overall
damage preventing measures. They all have a strong and natural interest in improved
availability. Hence, an increased cooperation with these parties is a possible and realistic
way forward.
It is also noted that other databases are being developed today. Such examples are the EU
project “ReliaWind”, the German “Offshore-WMEP” project and the Swedish VindForsk
project “V-344 RAMS-database for wind turbines”. The Swedish VindForsk project is a study
on how to structure a reliability database for wind turbines. A report will be published during
2010.
A short list with suggestions for an improved reliability database is presented below.
–
Incentives and means for operators/owners/service people to report to a reliability
database should be considered.
–
The reporting routines should be automatic and standardized. That is, the
information structure and terminology should be standardized.
–
Reliability data should be reported and presented on a detailed component level and
not on a general level.
–
It should preferably be possible to split the reliability data for each component into
more detailed subsystems. For example, a gearbox could be divided into several
subcomponents like gears, bearings, gears and shafts.
–
It should be considered to utilize a standardized product breakdown structure such
as RDS-PP[26].
–
Component reliability data should be possible to divide into turbine type and size,
technical concept like synchronous and asynchronous generators, manufacturing
year, external environmental conditions, IEC wind turbine class, failure cause
etcetera. For example, there are many different types of climate in Sweden and the
loads on a turbine can vary substantially with different climates. This means that
reliability data for one type of turbine might vary depending on where the turbine is
located.
It is noted that these suggestions agree well with those presented by Besnard [23] in
another VindForsk project.
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During this project, a simple version of how a wind turbine could be split up in different
components was made. This sketch for a database is shown in APPENDIX B. The proposed
structure aims at connecting the failure to a proposed damage preventing measure and may
be integrated in a database with a more general purpose. At a further stage the database
terminology should be adjusted to follow standards for product breakdown structures in
order to be more compatible with other databases. Such a standard could be RDS-PP by
VGB[26].
It is also strongly recommended that the future work on damage preventing measures is
not only based on failure statistics. Even if the available statistics were considerably better,
this would not suffice. It is strongly believed the physical conditions for wind turbines should
be investigated more thoroughly. The understanding of the impact of the load conditions
should be increased. Very little is known about the relation between failures and load
conditions. The current data gives little information on this important issue. Future studies
should focus on this relation. Moreover it should be interesting to study the connection
between failure and maintenance actions. It is temping to believe that some of the failures
could be avoided by simply following the maintenance instructions.
Damages on wind turbines occur due to damage mechanisms that are generally very well
known. There should be no question about that. The difference in the conditions for
component failure between the wind power industry and other industries should not be
large. Of course different industries have different traditions when it comes to maintenance,
inspection and condition monitoring. Comparing experience of damage preventing measures
from other industries, knowledge about the nature of the damage mechanism and current
practice in the wind industry will be an important tool in the evaluation of different damage
preventing measures. This will be done the following phases of this project.
34
ELFORSK
6
References
[1]
Driftuppföljning vindkraft, available at: www.vindstat.nu
[2]
Interview, Nils-Erik Carlstedt, VPC, March 2010
[3]
N-E Carlstedt, Vattenfall Power Consultant AB, Driftuppföljning av Vindkraftverk,
Årsrapport 2009
[4]
E-mail, Nils-EriK Carlstedt, VPC, March-May 2010
[5]
WindStats Newsletter web site, available at: www.windstats.com
[6]
E-mail, Jochen Keiler, Betreiber-Datenbasis, March-April 2010
[7]
WindStats Newsletter, Vol. 22, No. 4, Autumn 2009, ISSN 0903-5648
[8]
Data from Bill Canter, Editor at WindStats Newsletter, January 2010
[9]
E-mail, Stefan Faulstich, Fraunhofer Institute for Wind Energy and Energy Systems
Technology (IWES), March 2010
[10]
E. Echiavarra et al, Delft University of Technology and B. Hahn ISET, How Has
Reliability Of Technology Developed Through Time?, Paper for the European Wind
Energy Conference in Milan, Italy, May 2007
[11]
S. Faulstich et al, Windenergy Report Germany 2008, Institut für Solare
Energieversorgungstechnik (ISET), Kassel, Germany. Report available for order at
www.windmonitor.de
[12]
E-mail, Anders Stenberg, VTT, April 2010
[13]
F. Spinato, The Reliability of Wind Turbines, PhD Thesis, Energy Group, Durham
University, England, December 2008
[14]
Illustration
of
the
Bathtub
Curve,
available
http://www.weibull.com/hotwire/issue21/hottopics21.htm, 2010-07-06
[15]
A. Stenberg, Analys av vindkraftsstatistik i Finland,
universitetet Tekniska Högskolan, Esbo, 2010-02-05
[16]
P. J. Tavner et al, Durham university and G. J. W. van Bussel et al, Delft university
of Technology, Reliability of Different Wind Power Concepts with Relevance to
Offshore Application, Paper for the European Wind Energy Conference in Brussels,
Belgium, April 2008
[17]
Article
in
Ny
teknik,
published
2010-04-22,
available
at:
http://www.nyteknik.se/nyheter/energi_miljo/vindkraft/article766440.ece 2010-0526
[18]
S. Faulstich, P. Lyding, B. Hahn, IWES, Kassel, Germany, Electrical subassemblies
of wind turbines – a substantial risk for the availability, European Wind Energy
Conference, Warsaw, Poland, April 2010
35
Master
Thesis
at:
Aalto-
ELFORSK
[19]
J. Ribrant and L. Bertling, Survey of Failures in Wind Power Systems With Focus on
Swedish Wind Power Plants During 1997–2005, IEEE Transaction on energy
conversion, Vol. 22, No. 1, March 2007
[20]
Y. He, Study and Analysis of Distribution Equipment Reliability Data – Datastudier
och analys av tillförlitlighetsdata på komponentnivå för eldistributionsnätet, Elforsk
report 10:33, March 2010
[21]
S. Faulstich and B. Hahn, ISET, Comparison of different wind turbine concepts due
to their effect on reliability, UpWind, Deliverable WP 7.3.2, 2009-02-27
[22]
S. Faulstich, B. Hahn and P. Lyding, IWES, Kassel, Germany, P. Tavner, Energy
Group, Durham University, Reliability of offshore turbines – identifying risks by
onshore experience, European Offshore Wind, Stockholm, Sweden, September
2009
[23]
F. Besnard, Reliability studies of wind power systems and RAMS databases – Stateof-the-art, presentation at Vattenfall Stockholm, April 2010
[24]
L. Bertling, et al, Förstudie om tillförlitlighetsbaserat underhåll för vindkraftssystem
– Fokus på metoder för tillståndskontroll, Elforsk rapport 06:39, May 2006
[25]
European Standard EN 13306:2001, Maintenance terminology
[26]
VGB PowerTech e.V., www.vgb.org
36
ELFORSK
Appendix A. Data provided by Trygg Hansa.
Skademån Märke
Typ
Tillvår
Skada
0205
0205
0206
0208
0210
0210
0211
0211
0212
0303
0303
0303
0307
Vestas
Vestas
Vestas
Vestas
Vestas
Vestas
Vestas
Vestas
Vestas
Vestas
Vestas
Vestas
Vestas
V80- 2MW
V80- 2MW
V80- 2MW
V80- 2MW
V80- 2MW
V80- 2MW
V42-600KW
V80- 2MW
V80- 2MW
V80- 2MW
V80- 2MW
V80- 2MW
V44-600Kw
02
02
02
02
02
02
95
02
02
02
02
02
96
0308
Wind
World
Vestas
Vestas
Wind
World
Vestas
Wind
World
Vestas
600Kw
98
V80- 2MW
V47-660 kW
500Kw
02
00
96
Entreprenadskada. Påsegling. Skada på tornet
Entreprenadskada. Påsegling. Skada på tornet
Entreprenadskada 36 KV sjökabel
Entreprenadskada. Påsegling. Skada på tornet
Entreprenadskada. Påsegling. Skada på tornet
Stormskada under entreprenadtid.
Växellådshaveri. Hansenlåda
Entreprenadskada. Påsegling. Skada på tornet
Stormskada under entreprenadtid.
Åskskada under entreprenadtid.
Entreprenadskada
Entreprenadskada
Elektronikenhet(RCC styrning
rotorström)sönderbränd
Växellådshaveri. HSS lager. Märken från
åsknedslag
Åskskada på vinge under entreprenadtid.
Växellådshaveri. Lagerskador. Slitage
Generatorlager skurit med följdskador på axel
V80- 2MW
600Kw
02
98
Skada på vinge underentreprenadtid
Generatorlager havererat
3967
225KW
V42-600KW
W-3700
500kw
3700
500kW
V52
V52 850Kw
250Kw
92
95
95
Växellådshaveri.Drev släppt, orsakat en onormal
utmattningsspricka
Växellådshaveri. Hansenlåda
Växellådshaveri. Metallbit mellan kuggar.
96
Växellådshaveri. Lagerhaveri.
03
03
96
600Kw
98
Skador på kommunikationsmodul.
Generatorhaveri
Brand i maskinrummet. Totalskadat maskinrum.
Primärt lagerhaveri?
Växellådshaveri. Onormalt slitage lager?
V47
V44-600
V47
98
98
98
Växellådshaveri Flenderlåda.
Växellådshaveri.
Växellådshaveri Hansenlåda
0308
0403
0405
0407
0503
0503
0503
0610
0701
0703
0703
0703
0703
0704
0704
0704
Vestas
Wind
World
Wind
World
Vestas
Vestas
Wind
World
Wind
World
Vestas
Vestas
Vestas
37
ELFORSK
0704
0705
0705
0706
0706
0707
0708
0708
0708
0710
0711
0711
0711
0712
0712
0712
0712
0712
0712
0801
0801
0801
0801
0802
0802
0802
0803
0804
0804
0806
0806
0808
0810
0810
0810
Nordic
Wind
World
Wind
World
Vestas
Vestas
Vestas
Windworld
Wind
World
Vestas
Vestas
Vestas
Vestas
Neg
Micon
Vestas
Vestas
Vestas
Vestas
Vestas
Vestas
Wind
World
Wind
World
Windworld
Windworld
Wind
World
Wind
World
Micon
Vestas
Wind
World
Wind
World
Nordic
Windworld
Wind
World
Vestas
Vestas
Vestas
1000
500 nr 2
02
96
600kW
98
V44 600kW
V52 850Kw
V52
500
600Kw
96
03
03
95
98
Fel i hydrauliksystemet för vridningen av verket
Växellådshaveri. Innerringen på lager spruckit.
Följdskador i växellådan
Åsknedslag skadat integralmotor. Ny
typ+omformare pga inga reservd
Växellådshaveri
Elutrustning skadad
Generatorhaveri. Lagerskador.
Generatorhaveri Kortslutning Omlindn stator
Växellådshaveri pga felaktig olja
V39
V42 600kW
V42 600kW
V90
750Kw
95
96
96
07
00
Skada på Pitch-ackumulator
RCC skadad av elfenomen
Lager växellåda
Skiipack i VCS konvertern. Garantiskada
Växellådshaveri. Lagerskada.
V47
V47
V47
V47
V47
V52
600Kw
98
98
98
98
98
03
98
500
96
Växellådshaveri Hansenlåda.
Växellådshaveri Hansenlåda.
Växellådshaveri Hansenlåda.
Växellådshaveri Hansenlåda.
Växellådshaveri Hansenlåda.
Generatorhaveri Kortslutning
Växellådshaveri. Felaktig PT-100 givare orsakat
varmgång i lager
Generatorskada
W-3700
95
W-3700
600
95
99
Lager växellåda. Lådan nyrenoverad precis innan
skadan. Garanti?
Åska orsakat skada på vinge
Skadeanmälan tillbakadragen
W-4200 600
97
Generatorskada. Lager skurit
900
V44 600
250
01
99
97
Skada på transformator pga storm
Byte "Thermos" effektenhet
Kortslutning generator
600Kw
98
Växellådshaveri.
1000
500
250
02
95
97
Kommunikationsutrustning skadade pga åska
Generatorhaveri pga kortslutning
Växellåda
V44-600
V44
V42
97
98
Växellådsskada
Kontaktfel
RCC-enhet har slutat att fungera
38
ELFORSK
0810
0811
0811
0811
0811
0811
0812
0902
0903
0904
0906
0906
0906
0908
0909
0911
1001
1001
1003
Wind
World
Vestas
Vestas
Vestas
Vestas
Vestas
Vestas
Wind
World
Vestas
Nordic
Nordic
Wind
World
Vestas
Bonus
Vestas
Vestas
Vestas
Wind
World
Vestas
150
91
Klaff går inte in
V52
V39
V52
V52- 850 kw
V44
02
93
02
02
97
V47
600
01
98
Läckage trafo
Åsknedslag styrutrustning
Pump till växellåda havererat
Ljusbåge i transformatorn
Lager i kardan har skurit. Kulor har ramlat ur och har
antänt isolering
Växellådshaveri
Wire gått av. Utmattning
V52
1000
1000
250
03
02
02
96
Fel i generatorn. Garanti?
Åsknedslag hydraulikcylinder i vinge
Gummidämpare gått av
Åsknedslag i vingspets
V90
08
600
V47
V90
V44-600
3700-500
98
99
08
99
95
V44 600kW
96
Koppling mellan växellåda o generator havererat=
Garanti
Blixtnedslag
Överslag i brytare
Brandskada kopparskenor generator. Garanti
Lager skadat i växellådan.
Fel i värmare till växellådsoljan. Felaktig leverans
från början Låda 07
RCC skadad av elfenomen
39
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Appendix B. Suggestion for a database
structure.
The database should enable linking of the component to a proposed damage preventing
measure. Suitable for simple excel implementation. The structure also includes an initial
attempt to link with RDS designations.
Figure B1. Turbine Overview.
RDS
AAG
MDA
Component
Date
Damage
mechanism
(fatigue, wear,
corrosion etc)
Root
Cause
Consequence
Load
Type
Turbine
age
Turbine
Size
Proposed action
(inspection,
monitoring,
replacement
etc)
Medium
voltage
distribution
>1kV to
(30)35 kV
Rotor
system
MDA10
Rotor blades
MDA 11
Rotor blade
A
MDA 11
MQ001
Blade bolts
etc.
MDA 11
MQ001
Blade bolts
etc.
Falkenberg
2009
Gotland
2008
Fatigue
Fatigue
Untightened
bolts
Untightened
40
Roto blade
came off
Roto blade
came off
850 kW
Conv.
Tightening
Inspection
Conv.
Tightening
ELFORSK
bolts
MDA 12
MDA 13
MDA20
MDA20
UL001
MDC
MDC1x
GL001
MDC1x
UP001
Rotor blade
B
Rotor blade
C
Hub
Cast body of
hub (hub
bolts)
Blade
adjustment
Pitch drive
Blade
bearing
MDC10
Pitch system
MDC10
UP001
Pitch bearing
MDK
Drive train
MDK10
Rotor shaft
MDK10
UP001
MDK10
UQ001
Rotor
bearing
Clamping set
main shafts
MDK20
Gear
MDK20xx
yyy
Gear box
housing
Main gear
(gear
supports,
screws,
bearing,
shaft)
MDK20
TL001
MDK20
TL001
xxx
MDK20
TL001
xxx
MDK20
TL001
xxx
MDK20
TL001
xxx
MDK30
MDK30
QQN001
Gear box
teeth
Gear box
shafts
Gear box
bearings
Gear box
bolting
Brake
system
Brake
hydraulic
system
MDK30
xxx
Disc brake
MDK40
Couplings
MDK51
MDK51
EQ001
Main flow
gear oil
system
Gear oil
cooler
41
Inspection
ELFORSK
MDK51
BP001
MDL
MDL10
MDL10
BG010
MDL10
GL00x
MDL10
TL01
MDL10
TL01 OP01
MDL20
MDY
MDY10
MDY10
BS01
MDY10
KF001
Gear oil
pressure
cooling
system
Yaw gear
box
Azimuth
drive system
Wind
direction(Wi
nd vane)
Azimuth
drive
Azimuth live
ring
Azimuth
bearing
Azimuth
brake
system
Control and
protection
equipment
Electrical
control
system
Anemometer
CMS control
MK
Generator
system
MKA
Generator
MKA10
Generator
MKA10
yyy
MKA10
yyy
MKA10
yyy
MKA10
yyy
MKJ
MSA
MST
MUD
UMD
UMD10
Generator
stator
winding
Generator
rotor
winding
Generator
shaft
bearings
Generator
shaft
Generator
air cooling
Generator
transmissio
n
Generator
transforme
r incl.
Cooling
Machinery
enclosue
Structures
WTG
Tower
foundation
42
ELFORSK
UMD 20
UMD20
EA 001
UMD20
UB001
UMD20
UM2xx
Tower
Construction
Lightning
Cable carrier
system
Flanged
connection
ring xx (HVbolts, nuts)
43
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44