Periodic Report - Year 1

Wen-Ping Cao20th February 2016

Executive summaryThe 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 world-class 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 projectThe 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 mechanismsThis 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 modelsThis 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 faultsThis 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 activities1) 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 awardThe fellow has contributed to the following items.

1) Travel award, the 2015 IEEE International Magnetics Conference (INTERMAG), 11-15 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 methodologiesThis 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 faultsMachine 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 negative-sequence 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 low-intrusive online methods tend to lack accuracy, sensitivity or reliability.

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Control interface

Sensors

DFIGLoad

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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.

Healthy Faulted

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(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 faultsHealth monitoring techniques for power switching devices fall into two types: model-

based 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 on-state voltage after injecting a 100 mA current to the chip.

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GMR circuit

PMSM motor

Load

Air conditioner

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(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.

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(c)Fig. 5. Photograph of: (a) IGBT-based bridge inverter. (b) SiC-based converter. (c) In-situ CM

circuit.

IGBT in chamber

Controller

DAQ

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IGBTGate driver

ControllerSiC MOSFET

SiC SBD

Gate driver

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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 papers1) 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 modules-monitoring, 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 papers6) 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 5 th 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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