Periodic Report - Year 1
Wen-Ping Cao
20 February 2016
th
Executive summary
The Marie Curie fellow, Prof. Wen-Ping Cao, is grateful for the support from the FP7
under Marie Curie Fellowship scheme, which enables him to work at Massachusetts
Institute of Technology (MIT), USA, for the first 12 months; and return to the UK and
work for further 12 months. The outgoing phase is successful that he has received worldclass training on health monitoring and fault prediction technologies for wind turbines at
MIT and Georgia Tech. The fellow has generated 5 journal papers and 7 conference
papers, one grant and an award. As a result of his excellent research outcomes, the fellow
has been promoted to a Chair Professor at Aston University in Dec. 2015.
Aim and objectives of the project
The aim of this project is to develop an online health monitoring system for wind
turbines by understanding the failure models of electric machines and power converters
and by detecting their fault signatures in situ, involving the use of analytical, numerical,
and experimental methods. To achieve this, the major objectives are:
1) To study failure mechanisms of DFIGs and PMSGs by 3D finite element
electromagnetic and thermal analysis, with a focus on their winding short-circuit
faults.
2) To characterise mechanical faults in machine bearings and to analyse their
vibration-induced harmonic signals by fast Fourier transform (FFT).
3) To develop ageing models of IGBTs, SiC MOSFETs and dc-link capacitors used
for the wind turbine power converters, by PoF and 3D finite element tools.
4) To develop a harmonic-signature-based technique for detecting machine winding
faults; a vibration-signature-based technique for detecting mechanical bearing
faults; and an adaptive fault and lifetime prediction technique for power converters.
5) To develop a data fusion approach to identify early signs of fault generation and to
differentiate the different types of component failures. To develop an advanced
control scheme for coordinating the in situ measurements.
6) To set up experimental test rigs, carry out experimental evaluation of the proposed
technologies and algorithms, and integrate these into a 30-100kW wind turbine.
7) To propose a data communication system to acquire, process data of operational
conditions and the failure/ageing information of key components in wind turbines,
and to transmit these to a data server via wireless/satellite communications.
8) To inform the designers and manufacturers of generators and converters of research
findings, to disseminate the research outcomes and exploit commercial
opportunities.
In order to achieve the challenging objectives, the fellow has concentrated on WP1-3
in the first 12 months.
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WP1: Development of 3D machine models and study of their failure mechanisms
This WP is to gain a thorough understanding of fault generation and propagation in
the stator and/or rotor windings of several 30-100kW machines, by MagNet and
ThermNet. It is further broken down into four tasks, which have been achieved.
1.1 Developing analytical machine models upon different faults.
1.2 Developing 2D electromagnetic models.
1.3 Developing 3D thermal models.
1.4 Studying open and short-circuit winding faults.
WP2: Development and study of power switch and capacitor ageing models
This WP is to develop the IGBT, SiC and capacitor failure models by numerical
modeling (JMAG and Ansys Icepak) and experimental ageing tests (using a thermal
chamber). It is further broken down into four tasks, which have been achieved.
2.1 Thermo-mechanical modelling of IGBTs.
2.2 Thermo-mechanical modelling of SiCs.
2.3 Thermo-mechanical modelling of capacitors.
2.4 Conducting ageing tests to establish ageing/failure models.
WP3: Establishing the correlation between PWM-induced harmonics and winding
faults; terminal characteristics of power switches/capacitors and their
degradation; vibration-induced harmonic signatures and mechanical bearing
faults
This WP is to establish the link between failure/ageing and fault signatures
(observed from terminals) using intrusive or offline measurements. It is further
broken down into two tasks, which have been achieved.
3.1 Correlating PWM-harmonics and winding fault signatures.
3.2 Correlating vibration-FFT and bearing fault signatures.
3.3 Establishing terminal characteristics and power converter ageing.
3.4 Developing in-situ low-intrusive technologies.
Other activities
1) The fellow talked with all professors in the EECS School of MIT, who have expertise
in electrical machines, power electronics and condition monitoring techniques.
2) The fellow received academic visitors from Denver University (Z. Liu) to discuss
wind turbine technologies and Newcastle University (V. Pickert) on new condition
monitoring and packaging technologies for power electronic devices.
3) The fellow received an academic visitor (Lassi Aarniovuori) from Lappeenranta
University of Technology (LUT), Finland.
4) The fellow entered into Annual MIT-CHIEF Business Plan Contest at MIT and my
proposal was selected as a semi-finalist, in Aug. 2015.
Funding and award
The fellow has contributed to the following items.
1) Travel award, the 2015 IEEE International Magnetics Conference (INTERMAG), 1115 May 2015, Beijing, China
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2) Royal Society-NSFC, Newton Fund, with Shenyang University of Technology,
“Optimal design & control of brushless electricity-excited synchronous generator”,
April 15-March 2017
Developed methodologies
This proposal will develop novel condition monitoring technologies for understanding
failure mechanisms and detecting/predicting fault generation of key electrical
components in wind turbine generators, with a primary focus on machine windings,
bearings, and power switching devices whilst still making it possible for the inclusion of
other electrical components (e.g. inductors and capacitors) and other types of faults. The
developed new technologies will be integrated into the existing electrical circuitry and
power drivetrain of wind turbines. The technologies are principally proven by the
fellow’s pioneering work on PMSMs, IMs, switched reluctance machines (SRMs);
IGBTs, MOSFET devices and power converters with regard to single isolated fault
mechanisms of critical components at laboratory environments.
During the outgoing phase, electro-thermal models of electrical motors were
developed in finite element software MagNet and ThermNet, IGBT models in Ansys
Icepak, and motor drive models in Matlab/Simulink. Although individual models were
validated by simulation and experimental results, the interaction of the integrated
components is yet to be understood. More specifically, existing methods rely on invasive
and offline measurements in the laboratory environments involving external power
supplies and signal generators. Therefore, a number of new in-situ methods are put
forward to integrate condition monitoring functions into the existing systems including
giant magnetoresistance (GMR) stray flux measurement, pulse-width modulation (PWM)
harmonics-based machine winding fault detection, and adaptive lifetime prediction for
power switches and converters. In the full implementation, a sensor network-based data
fusion method will be developed to distinguish different types of component faults; and a
data-driven approach will be implemented to map out the damage space for main failure
models of various key components. Ageing features of IGBT, SiC and GaN switches will
then be extracted to specify the current degradation level (state of health), and damage
growth models will be employed to estimate the damage accumulation and remaining
lifetime at given predicted operational conditions. A system-level control scheme has
developed to regulate the required measurements and interface the measurement
circuitry.
(a) Detection of machine faults
Machine failures have been a heated research topic for a long time. In the field, the
main causes of failure are winding (37%) and bearing faults (40%). Winding short
circuits generally start with a turn-to-turn fault due to insulation deterioration or adverse
operation and may grow into more severe faults if left untreated. These faults include
open-circuits and short-circuits, and result in unbalanced magnetomotive force (mmf)
which can be identified from either the airgap flux or leakage flux via direct
measurement. GMR sensors are very effective to detect low-level magnetic flux (to few
mT) where search coils may struggle. See the measuring circuits for a search coil and
GRM sensors in Fig. 1.
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(a)
(b)
Fig. 1. Measurement of stray flux by: (a) Search coil. (b) GMR sensors.
GMRs are based on a quantum mechanical magnetoresistance effect and its
discoverers were awarded the 2007 Nobel Prize in physics. On this basis, the PI has
developed an inexpensive stray flux measuring circuitry and set up a test rig at
Massachusetts Institute of Technology (MIT) to study winding faults, as shown in Fig.
2(a). Test results for stray flux spectrum of an induction machine with turn to turn faults
are presented in Fig. 2(b). Static and transient feature extraction is applied to the flux
results and harmonic contents are obtained from the resulting spectrogram by short-time
Fourier transform as follows.
+∞
S𝑥 (𝑡, 𝑣) = |∫−∞ 𝑥(𝑠) ∗ ℎ(𝑠 − 𝑡) ∗ 𝑒 −𝑖∗2𝜋𝑣𝑠 𝑑𝑠|
(1)
The test results are effective to correlate the winding faults with leakage flux changes.
Alternatively, fault detection can be achieved by extracting zero-sequence negativesequence or third-harmonic components in fault signatures. Other techniques such as
partial discharge, artificial intelligence, wavelet transform and harmonic injection have
also been attempted for electrical machine fault detection. However, some of these
methods are offline and unviable for in-situ online measurements whilst those lowintrusive online methods tend to lack accuracy, sensitivity or reliability.
Control interface
Sensors
DFIG
Load
(a)
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Healthy
Faulted
(b)
Fig. 2. (a) Experimental setup for winding fault tests at MIT. (b) Stray flux spectrum of induction
machine with turn to turn faults.
Based on the fellow’s work on stray load loss, harmonic loss and spectral analysis, an
improved harmonic injection technique has been developed to understand the terminal
characteristics of winding faults in the proposed work. As shown in Fig. 3(a), it utilises a
regenerative Ward-Lenard system to provide a perfectly sinusoidal power supply and
thus an injected harmonic voltage of chosen frequency and amplitude can be
superimposed on the clean fundamental. At the high frequency of the injected harmonic
(kHz), the winding impedance is dominated by its inductance which can be derived from
extracting the resulting high frequency components in the terminal current. Its sensitivity
has been partially validated at Nottingham. This project has further developed a new
harmonic injection approach without a need to inject external harmonics. Instead, it
utilises those high-frequency harmonics resulting from the PWM of voltage-source
converters (VSCs). There are some characteristic harmonics in the PWM voltages with
reasonable magnitude to be used for fault detection. For a given line voltage spectrum
from the inverter in actual inverters (see Fig. 3b), the extra harmonics are viewed as
injected signals and their fault signatures in the inductance can be extracted for data
analytics. Upon a short- or open-circuit fault, the change in the winding inductance can
be identified by the fast Fourier transform analysis of the harmonic currents resulting
from PWM-induced voltage harmonics. A look-up table on space modulation profiling
will be created to monitor these changes in inductances and flag up any incipient
winding-related faults.
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(a)
200
180
Harmonic voltage (V)
160
140
120
100
80
60
40
20
0
0
5000
10000
15000
20000
25000
Frequency (Hz)
(b)
Fig. 3. (a) Harmonic injection test rig. (b) Line voltage spectrum from the inverter.
Similarly, bearing faults can also cause an air-gap eccentricity, an unbalanced mmf as
well as vibration and noise. The fellow has set up another test rig at Georgia Institute of
Technology (Georgia Tech.) to measure mechanical faults in a PMSM motor, as shown
in Fig. 4(a). Preliminary tests have shown excellent stray flux results (Fig. 4b and 4c)
from the test motor with several artificial mechanical faults.
This project has also adopted an improved signal processing method based on standard
accelerometers to detect the stator frame vibration. Its effectiveness has been initially
proved at Nottingham and MIT.
In terms of machine faults, this research has set out to observe the stator winding
inductance, leakage flux and mechanical vibration, the three fault indexes being used in
combination by a sensor fusion approach and digital signal analysis techniques to detect,
locate and differentiate the faults arising from stator windings, rotor eccentricity and
bearing faults.
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(b) Detection of power switch faults
Health monitoring techniques for power switching devices fall into two types: modelbased and data-driven approaches. The former utilise physics of failure (PoF) models,
which define the relationship between the damage or ageing of a component and its
actual life loading conditions, and then estimate the current health condition and the
remaining useful life (RUL). However, their coefficients need to be determined from
experiments which introduce errors from assumptions, measurements, filtering, ageing
and curve fitting. High-fidelity models incorporating degradation phenomena over the
device lifespan are cumbersome and computationally demanding, making them
unpopular for real-time applications. Furthermore, these PoF models neglect coupling
effects between interlinked fatigue mechanisms which can escalate degradation.
Alternatively, data-driven methods usually incorporate pattern recognition and machine
learning to extract the prognostic signatures from the monitored parameters and to
correlate the data with the damage growth. They do not rely on ageing models and can be
easily implemented for multi-parameter failure problems. But they can not provide
lifetime prediction. Therefore a hybrid method is considered in this work to combine the
merits of the two methods. The preliminary work has built an IGBT bridge inverter (Fig.
5a), a SiC-based converter (Fig. 5b), and a condition monitoring circuit (Fig. 5c) for the
EV inverter.
Preliminary work has provided in insight into solder fatigue and bondwire faults of
IGBTs. This work has made use of existing equipment and to develop an adaptive fusion
method by combining model-based and data-driven methods. An accurate PoF model has
been developed from offline tests in the laboratory for life-consumption prediction and
will be dynamically updated by the data-driven method. The device’s on-state voltage in
relation to a junction temperature is measured when controlling the amplitude of the load
current (50-100 A) at low speeds. The junction temperature is determined from the onstate voltage after injecting a 100 mA current to the chip.
PMSM motor
Air
conditioner
GMR circuit
Load
(a)
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(b)
(c)
Fig. 4. (a) Experimental setup for unbalanced loading at Georgia Tech. (b) Stray flux spectrum. (c)
RMS and peak values of the induced voltage from GMR sensors.
IGBT
Gate driver
SiC SBD
SiC MOSFET
Gate driver
Controller
(a)
(b)
DAQ
Controller
IGBT in
chamber
Pulse current
(c)
Fig. 5. Photograph of: (a) IGBT-based bridge inverter. (b) SiC-based converter. (c) In-situ CM
circuit.
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Although IGBTs’ failiure models have been studied for some time, the failiure
mechanisms of SiC and GaN transistors are still an uncharted territory. In theory, SiC
devices perform better in harsh automotive environments and can be considered as the
future power device for EVs. However, their junction temperature is generally
determined from the threshold voltage, different to the on-state voltage for IGBTs.
Similarly, GaN devices can also offer high efficiency and compact module design but
presently suffer from low power ratings. This research will be the first international effort
to develop and monitor SiC- and GaN-based inverters for automotive applications. The
results will open up many opportunities for new devices and new converter designs.
Prelinary test results have been piblished in major journals and conferences as follows.
More results will be published in future conferences and journals.
Published journal papers
1) Z. Tan, X. Song, W. Cao*, Z. Liu, Y. Tong, “DFIG machine design for maximizing power
output based on surrogate optimization algorithm,” IEEE Transactions on Energy
Conversion, Vol. 30, Issue 3, pp. 1154-1162, 2015.
2) F. Zhang, G. Jia, Y. Zhao, Z. Yang, W. Cao, J. Kirtley, “Simulation and experimental
analysis of a brushless electrically excited synchronous machine with a hybrid rotor,” IEEE
Transactions on Magnetics, Vol. 51, No. 12, Article 8115007, Dec. 2015
3) B. Ji, W. Cao*, V. Pickert, “State-of-the-art intelligent gate drivers for power modulesmonitoring, control and management at the heart of power converters,” Book Chapter in
Control Circuits in Power Electronics: Practical Issues in Design and Implementation,
Chapter 11, IET, UK, 2015.
4) B. Ji, X. Song, E. Sciberras, W. Cao, Y. Hu, V. Pickert, “Multiobjective design optimization
of IGBT power modules considering power cycling and thermal cycling,” IEEE Transactions
on Power Electronics, Vol. 30, Issue 5, pp. 2493-2504, May 2015.
5) B. Ji, X. Song, W. Cao*, V. Pickert, Y. Hu, J. Mackersie, and G. Pierce, “In situ diagnostics
and prognostics of solder fatigue in IGBT modules for electric vehicle drives,” IEEE
Transactions on Power Electronics, Vol. 30, Issue 3, pp. 1535-1543, Mar. 2015.
Published conference papers
6) L. Aarniovuori, W. Cao, H. Chen, N. Yang, “New shunt calorimeter for testing electric
motors,” the 2015 IEEE Industry Applications Conference (IAS) Annual Meeting, Dallas,
USA, 18-22 Oct. 2015
7) H. Chen, W. Cao, P. Bordignon, R. Yi, H. Zhang, W. Shi, “Design and testing of the world’s
first single-level press-pack IGBT-based submodule for MMC VSC HVDC applications,” the
7th Annual IEEE Energy Conversion Congress & Exposition (ECCE’15), Montreal, Canada,
20-24 Sep. 2015
8) J. Si, L. Xie, W. Cao, X. Zhang, H. Feng, “Magnetic analysis and parameters calculation for
solid-rotor induction motor coated with copper layer,” the 2015 Conference on the
Computation of Electromagnetic Fields (Compumag’15), Montreal, Canada, 28 June-2 July,
2015
9) Y. Zhang, W. Cao, J. Morrow, “Dual three-phase induction motor power loss analysis with
its modeling and simulation of vector-controlled system,” the 5th annual IEEE international
Conference on CYBER Technology in Automation, Control and Intelligent Systems,
Shenyang, China, 8-12 June 2015
10) N. Yang, W. Cao, Z. Liu, Z. Tan, Y. Zhang, S. Yu, J. Morrow, “Novel asymmetrical rotor
design for easy assembly and repair of rotor windings in synchronous generators,” the 2015
IEEE International Magnetics Conference (INTERMAG), Beijing, China, 11-15 May, 2015
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11) M. Si, X. Yang, S. Zhao, J. Si, W. Cao, “Modeling and analysis of the magnetic field of a
surface-interior permanent magnet synchronous motor,” the 2015 IEEE International
Magnetics Conference (INTERMAG), Beijing, China, 11-15 May, 2015
12) N. Yang, W. Cao, Y. Hu, “New machine design for easy insertion of excitation coils in
synchronous generators,” the IEEE International Electric Machines & Drives Conference
(IEMDC 2015), Idaho, USA, 10-13 May, 2015
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