Thesis submitted for the degree of Ph.D. in Engineering Sciences

Brussels School of Engineering

Bio, Electro And Mechanical Systems (BEAMS) department

Thesis submitted for the degree of Ph.D. in Engineering Sciences

Fault-tolerance and noise and vibration aspects of electrical drives

Application to wind turbines and electrical vehicle traction

Yves Mollet

Thesis Director: Prof. Johan Gyselinck President of the committee: Prof. Jean-Claude Maun Members of the committee: Prof. Michel Kinnaert

Prof. Philippe Lataire

November 2017

The awareness of the human responsibility in global warming has led to various private and public initiatives to reduce the emission of greenhouse gases, up to international level. In this context the development of renewable technologies in two sectors having an important ecological footprint, i.e.

production of electricity and transportation, is targeted.

In the firstly mentioned sector, the progression of wind energy is at present the most rapid among all renewable energies. But wind turbines still suffer from a global lack of reliability and accessibility compared to classical power plants, leading to potentially important production losses and repair costs. The first part of the present work focuses on the improvement of the electrical chain reliability by the combination of an estimator and a fault-detection algorithm to achieve sensor-fault tolerance, taking benefit from the already available measurement redundancies on doubly-fed-induction-machine (DFIG) drives.

Estimators and sensor-fault detection and isolation (FDI) in DFIGs have been the object of many research papers. However, most of them only consider one unique type of measurement and only a few works consider magnetic saturation. A new combination of a closed-loop observer with a cumulative- sum-based FDI technique, considering magnetic saturation and using limited computational resources is proposed here to estimate electromagnetic torque, rotor currents and position for sensor-fault detec- tion and tolerance. This algorithm is then validated in steady state and in case of moderate transients, unbalanced conditions and misestimation of DFIG parameters. The estimator can also start on the fly during the start-up process of the generator.

In the transportation sector, new hybrid and full-electric vehicles start to be visible on the roads, but still need important technological improvements in terms of autonomy, performances, but also produced noise and vibrations. The objectives of the second part of this doctoral thesis are related to this last challenge and consist of the experimental investigation of noise, vibration and harshness (NVH) aspects of an 8/6 switched-reluctance machine (SRM) designed for an electrical vehicle (EV).

The NVH issues of SRMs, limiting their usage in automotive and other domains, have been the subject of various papers. However, most of them focus on modal analysis or detailed phenomena, while a global evaluation of NVH aspects of SRMs in normal working conditions is rarely made, as well as the use of reproducible sound metrics. A global and relatively fast experimental method to assess the evolution of noise and vibration is proposed. Tests are performed in transient regime, using continuously varying working conditions when possible, for the excitation of a large band of frequencies.

The resulting current, radial vibration and acoustic noise are presented as spectrograms for an easy distinction of affected and unaffected frequencies and compared with the associated loudness and sharpness.

Furthermore, the implementation of a new faster-sampled current-hysteresis controller has allowed to improve the quality of the control and of the acoustic noise by reducing the current-ripple amplitude and the excitation of resonances. The various tests show that the switching frequency has to be high enough to avoid exciting the ovalization mode of the SRM, but not too high to avoid producing a too sharp noise. The ripple amplitude also has to be considered to limit the loudness. Therefore, soft chopping, or a reduced DC-bus voltage at low speeds, has to be preferred with a relative small hysteresis bandwidth. Finally, the case of an open-phase fault has been investigated showing amplified even current orders in the vibration and acoustic-noise plots.

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La prise de conscience de la responsabilit´ e humaine dans le r´ echauffement climatique est ` a la source de nombreuses initiatives publiques et priv´ ees parfois internationales pour r´ eduire les ´ emissions de gaz

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a effet de serre. Dans ce contexte, le d´ eveloppement de technologies durables dans deux secteurs ` a forte empreinte ´ ecologique est vis´ e: la production d’´ energie ´ electrique et les transports.

Dans le premier secteur, la progression de l’´ eolien est ` a pr´ esent la plus rapide parmi toutes les ´ energies renouvelables. Cependant, les ´ eoliennes souffrent d’un manque global de fiabilit´ e et d’accessibilit´ e par rapport aux centrales ´ electriques classiques, ce qui conduit potentiellement ` a des pertes de production et des coˆ uts de r´ eparation importants. La premi` ere partie de ce travail se fo- calise sur l’am´ elioration de la chaˆıne ´ electrique en la rendant tol´ erante aux d´ efauts de capteurs au moyen de la combinaison d’un estimateur et d’un algorithme de d´ etection de d´ efauts, tirant avantage de la redondance de mesures d´ ej` a pr´ esente sur les entraˆınements ` a machines asynchrones ` a double alimentation (MADA).

Les estimateurs et la d´ etection et l’isolation de d´ efauts de capteurs sur les MADA a fait l’objet de nombreuses publications scientifiques. Cependant, la plupart d’entre elles consid` erent un seul type de mesure et peu de travaux prennent en compte la saturation magn´ etique. Une nouvelle combinaison d’un observateur et d’un algorithme de d´ etection de d´ efauts de type ‘CUSUM’, consid´ erant la satu- ration magn´ etique et n´ ecessitant une puissance de calcul limit´ ee, est propos´ ee dans cette th` ese pour l’estimation du couple ´ electromagn´ etique, des courants et de la position rotoriques en vue d’obtenir la tol´ erance aux d´ efauts de capteurs. Cet algorithme est valid´ e en r´ egime permanent et cas de tran- sitoires mod´ er´ es, de tensions du r´ eseau d´ es´ equilibr´ ees et d’erreurs d’estimation des param` etres de la MADA. L’estimateur est aussi capable de d´ emarrer seul lors du d´ emarrage de la g´ en´ eratrice.

Dans le secteur des transports, des v´ ehicules hybrides et ´ electriques commencent ` a ˆ etre visibles sur les routes, malgr´ e que des progr` es technologiques importants en termes d’autonomie, de perfor- mances, mais aussi de bruits et vibrations soient encore n´ ecessaires pour une utilisation plus intensive.

L’objectif de la deuxi` eme partie de cette th` ese se rapporte ` a ce dernier d´ efi et consiste ` a analyser les aspects acoustiques et vibratoires d’une machine ` a r´ eluctance variable 8/6 con¸ cue pour propulser un v´ ehicule ´ electrique.

Ces probl` emes acoustiques et vibratoires, qui limitent notamment l’usage de telles machines dans des applications de propulsion, ont ´ et´ e l’objet de divers articles scientifiques. Cependant, la plu- part d’entre eux sont focalis´ es sur des analyses modales ou de ph´ enom` enes particuliers, alors qu’une

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evaluation globale des probl` emes de bruit et de vibration des machines ` a r´ eluctance variable en con- ditions normales de fonctionnement est rarement propos´ ee, de mˆ eme que l’utilisation de crit` eres de qualit´ e sonore. Une m´ ethode exp´ erimentale globale et relativement rapide pour ´ evaluer l’´ evolution du bruit et des vibrations est propos´ ee dans ce travail. Les essais sont r´ ealis´ es en r´ egime transitoire pour exciter une large bande de fr´ equences et en faisant varier continuellement, quand cela est possible, les conditions de fonctionnement. Les courants, vibrations radiales et bruits acoustiques r´ esultants sont pr´ esent´ es sous formes de spectrogrammes pour une distinction ais´ ee des fr´ equences affect´ ees et non-affect´ ees et compar´ es aux niveaux calcul´ es de bruyance et d’acuit´ e correspondants.

Par ailleurs, la mise en place d’un nouveau r´ egulateur ` a hyst´ er` ese en courant ` a plus grande fr´ equence d’´ echantillonnage a permis d’am´ eliorer la qualit´ e de la commande et du bruit acoustique associ´ e en r´ eduisant l’amplitude des oscillations de courant et l’excitation des fr´ equences de r´ esonance. Les essais montrent que la fr´ equence de commutation doit ˆ etre suffisamment ´ elev´ ee pour ´ eviter l’excitation du mode d’ovalisation de la machine, mais pas trop pour ´ eviter une trop grande acuit´ e du son produit.

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L’amplitude des oscillations doit aussi ˆ etre consid´ er´ ee pour limiter la bruyance. En cons´ equence, une commande en ‘soft chopping’, ou une tension r´ eduite du bus continu ` a basse vitesse, doit ˆ etre combin´ ee

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a une bande d’hyst´ er` ese relativement faible. Enfin, le cas d’un d´ efaut de phase ouverte a ´ et´ e ´ etudi´ e et

a montr´ e une amplification des ordres pairs du courant dans les spectres vibratoires et acoustiques.

De bewustwording van de menselijke verantwoordelijkheid in de opwarming van de aarde heeft tot ver- schillende private en publieke initiatieven geleid om de uitstoot van broeikasgassen te verminderen. In deze context is de ontwikkeling van hernieuwbare technologie¨ en hoofdzakelijk gericht op twee sectoren met een belangrijke ecologische impact: elektriciteitsproductie en transport.

In de eerste sector ontwikkelt windenergie zich op dit moment sneller dan alle andere hernieuwbare energie¨ en. Maar windturbines lijden nog steeds aan een gebrek aan betrouwbaarheid en toegankeli- jkheid, en dus aan potentieel hogere productieverliezen en herstelkosten, als ze met klassieke kracht- centrales worden vergeleken. In het eerste deel van deze doctoraatsthesis wordt op de verbetering van de elektrische keten geconcentreerd door de combinatie van een schatter en een foutdetectie- en -isolatiealgoritme (FDI-algoritme) om sensorfouttolerantie te verkrijgen dankzij de reeds aanwezige meetovertolligheid op dubbelgevoede inductiemachine (DFIG) aandrijvingen.

Schatters en sensor-FDI-algoritmen zijn het onderwerp van vele wetenschappelijke artikelen ge- weest. Meestal wordt maar ´ e´ en sensortype beschouwd en met de magnetische verzadiging wordt niet vaak rekening gehouden. Een nieuwe combinatie van een schatter met gesloten terugkoppeling en een FDI-techniek gebaseerd op het ‘cumulative-sum’ principe is voorgesteld. Zo kan het elektromag- netische koppel, de rotorstromen en positie worden geschat voor sensor FDI en fouttolerantie met beperkte rekenkosten en zonder de magnetische verzadering te verwaarlozen. Het algoritme wordt in stabiele toestand gevalideerd, maar ook in het geval van gematigde transi¨ ente situaties, onevenwichtige netwerkomstandigheden en een verkeerde schatting van DFIG parameters. Het kan ook vanzelf starten tijdens de startprocedure van de generator.

In de vervoersector beginnen hybride en elektrische voertuigen op de wegen te rijden. Maar voor een intensiever gebruik van zo’n wagens zijn er nog technologische verbeteringen nodig met betrekking tot autonomie, prestaties en ook geluid en trillingen (NVH). Het tweede deel van de thesis betreft die laatste uitdaging en bestaat uit het experimentele onderzoek van geluid en trillingen op een 8/6 variabelereluctantiemachine (SRM) ontwikkeld voor elektrische voertuigen.

De NVH-problemen van SRM’s beperken hun gebruik in automobiele en andere toepassingen en onderzoek wordt erover voortgezet. Vele wetenschappelijke artikelen focussen toch op modale analyse of gedetailleerde fenomenen terwijl een globale evaluatie van NVH aspecten in SRM’s in gewone operatiecondities nauwelijks wordt gemaakt. Hetzelfde geldt voor het gebruik van reproduceerbare geluidsmetrieken. Een globale en vrij vlugge experimentele methode is hier voorgesteld om het NVH gedrag te schatten. Testen worden in transi¨ ente situaties uitgevoerd om een brede frequentieband te exciteren, indien mogelijk met voortdurend vari¨ erende condities. De gemeten fasestroom, trilling en geluid worden als spectrogrammen geplot om het verschil tussen be¨ınvloede en niet geaffecteerde frequenties te vergemakkelijken en met de berekende akoestische luidheid en scherpte vergeleken.

Bovendien heeft de implementatie van een sneller bemonsterd stroomhysteresisregelaar geleid tot een verbetering van de regulatie- en akoestische kwaliteit door de amplitude van de stroomrimpeling en de excitatie van resonantiefrequenties te verminderen. De testresultaten tonen dat de schakelfrequentie voldoende hoog moet zijn om de excitatie van de ovale vervormingsmode te vermijden, maar niet te hoog om de scherpte van het geluid te beperken. De amplitude van de rimpel be¨ınvloedt ook de luidheid en daarvoor moet in aanmerking worden genomen. Bijgevolg zou ‘soft chopping’ mode, of een lagere spanning op de DC-bus bij lage toerentallen, met een relatief klein hysteresisband beter worden gebruikt. Uiteindelijk wordt het geval van een openfasefout bestudeerd en onthult versterkte gelijke frequentievolgorden in de trilling- en geluidplots.

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This Ph.D. dissertation has been the outcome of years of research in the Bio-, Electro- And Mechanical Systems department of the Universit´ e Libre de Bruxelles. I would like to thank the Walloon region and the European Commission for having financed the first and the second part of the present work through the POWER (Pˆ ole de comp´ etitivit´ e MECATECH, agreement nr. 6718) and DeMoTest-EV (FP7-PEOPLE-2012-IAPP, agreement nr. 324345) projects respectively.

I am also very thankful to Prof. Johan Gyselinck: first, to have given to me the rare opportunity, as an industrial engineer, to start a Ph.D. at ULB and second, to have let me go away to the Von Karman Institute for a one-year Research Master and welcomed me back afterwards. Third, I am also grateful for the rigour but also the flexibility and the confidence, which characterized his supervision.

I also thank the members of my committee: Prof. Jean-Claude Maun, Prof. Michel Kinnaert and Prof. Philippe Lataire for their comments and help related to this work, but also to other tasks.

Regarding the first part, I also have to thank Maintenance Partners Wallonie and the SAAS department of the ULB for their help in the frame of the POWER project (visit of a wind turbine, access to field data and to a simulation model). Among them, I can mention Mr. Philippe Mol, Mr.

Laurent Rakoto and Mr. Laurent Catoire.

Regarding the second part, I also have to thank the different partners of the DeMoTest-EV project for their collaboration: (among others) Mr. Mathieu Sarrazin and Prof. Herman Van der Auweraer for the local (intensive) and high-level supervision at Siemens Industry Software, where I made a one-year secondment, Dr. Paul Minciunescu, Mr. Bogdan Varaticeanu, Mr. Costi Nicolescu, Mr. Silviu &

Mrs. Florentina Matei for the technical and/or moral support at ICPE, where I made three months of secondment, Prof. Claudia Mart¸i¸s from UTCN for the coordination of the project and Aron Popp and (last but not least) Sebastian Ciceo for some helpful contributions to some tasks (outside the scope of this thesis) and also some good time and moral support. I am also thankful to two of my previous master thesis students: Laurent Placet and Santiago Manso Camp from whom I integrated some results in the present thesis. It has to be noted that Laurent also contributed in a non-negligible way to the improvement of the SRM test bench.

Regarding the writing phase of the Ph.D., I have also to thank Prof. Ruth V. Sabariego (KUL), for her helpful advices regarding the organization of my time during this period. I also have to thank my colleague Marcelo Nesci Soares, my former colleague Dr. Cis De Maesschalck, my sister Catherine, Mr. David Kimplaire (teaching electrical machines and power electronics at ECAM) and Prof. Claude-Emile Dierickx (retired professor at ECAM) to have contributed to the proofreading process.

I am also grateful to the successive colleagues I had during the whole time I spent in the laboratory for the good atmosphere and mutual aid for the realization of various tasks (e.g. the replacement of the didactic benches) and outside the scope of work (e.g. badminton): Yannick, Quentin, M´ elik, Benoˆıt, Gilles, Julien; Mircea, Adrian, Vlad; Prof. Olivier Mortehan, Fr´ ed´ eric; Mohamed; Micha¨ el, Jawwad, Fabien (with a special thank to his contribution in some of my papers); Alicia, Olivier, Matthieu, Pierre; Martin, Thomas D., Audric; Thomas G., Fran¸ cois; Fernando, Ander, Ram´ on; Wei, Yuxue, Dongmin, Xiang; Xavier, Thomas R., Neriton; Diogo, Machado, Marcelo. I also need to mention Ariane for her administrative work and moral support, Pascal for his technical help and Christophe for further technical help and for his numerous practical explanations regarding the working principle of CNC and manual machines, specific tools, 3D printers...

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I would like to thank the ‘VKI team’ for their help and moral support during the research master, but sometimes also afterwards: my promoter and supervisor Guillermo Paniagua and Henny Bottini, as well as my previous colleagues: (among others) Guerric, Laura, Cis, Maria Chiara, Fabrizio, Alessia, Tim, Martin, Anik´ o, Vincent, Mathieu, Clara, Jorge...

I am thankful to my family (parents, grandmother, brother, sister, sister-in-law, uncles, aunts, cousins) and best friends (Emmanuel, Cl´ emence, Gregory) for their moral support, with a special thank to my uncle Guy, who recommended me to start a Ph.D. thesis. Since they have also motivated me to start a research career before I finished my master, I need also to thank Mr. Jean Svaldi (ECAM) and Mr. S´ ebastien Paris (VKI).

Last but not least, I finally want to thank all people who brought me any kind of help and who I unfortunately forgot in this section...

“Then I saw all labour and every skilful work, that for this a man is envied of his neighbour. This

also is vanity and a striving after wind.” Qo 4,4

Abstract ii

R´ esum´ e iii

Overzicht v

Acknowledgements vi

Contents viii

Acronyms xv

List of Figures xxi

List of Tables xxx

0 Introduction 2

0.1 Background . . . . 2

0.1.1 Global climate challenge . . . . 2

0.1.2 Evolution of wind energy sector . . . . 2

0.1.3 POWER Project . . . . 5

0.1.4 Evolution of electrical-vehicle sector . . . . 6

0.1.5 DeMoTest-EV Project . . . . 7

0.2 Motivation . . . . 9

0.2.1 Fault detection and isolation, with focus on DFIGs in WECS context . . . . . 9

0.2.2 NVH issues on SRMs with focus on EV applications . . . . 10

0.3 Structure and contributions of the thesis . . . . 10

0.3.1 Fault-tolerant DFIG drives for wind energy conversion systems . . . . 11

0.3.2 NVH aspects of electrical drives for EVs . . . . 11

I Fault-tolerant DFIG drives for wind energy conversion systems 13 1 Principles of wind energy conversion 14 1.1 Introduction . . . . 14

1.2 Characterization and modelling of the wind . . . . 15

1.2.1 Distribution of wind power potential around the world . . . . 15

1.2.2 Average and turbulent components of the wind speed . . . . 15

1.2.3 Wind-speed distribution . . . . 16

1.2.4 Wind profile along the altitude . . . . 17

1.2.5 Wind-turbulence model . . . . 18

1.3 Mechanical-energy extraction . . . . 18

viii

1.3.1 Principle and modelling of wind-power extraction . . . . 18

1.3.1.1 Equivalent wind speed and wind turbulence . . . . 18

1.3.1.2 Blade-element model . . . . 19

1.3.1.3 Actuator-disc model . . . . 21

1.3.2 Wind-power-extraction control . . . . 23

1.3.2.1 Fixed-speed wind turbines . . . . 23

1.3.2.2 Variable-speed wind turbines . . . . 23

1.4 Principal wind-turbine-drive configurations . . . . 25

1.4.1 Induction generator drives . . . . 25

1.4.1.1 Squirrel-cage induction generators (SCIGs) . . . . 25

1.4.1.2 Wound-rotor induction generators (WRIGs) with rotor-resistance vari- ation . . . . 26

1.4.1.3 Doubly-fed induction generators (DFIGs) . . . . 27

1.4.2 Synchronous-generator drives . . . . 28

1.4.2.1 Wound-rotor synchronous generators (WRSGs) . . . . 28

1.4.2.2 Permanent-magnet synchronous generators (PMSGs) . . . . 29

1.5 Summary . . . . 29

2 DFIG wind generator, power converter and grid connection – modelling and con- trol 31 2.1 Introduction . . . . 31

2.2 Doubly-fed induction generator (DFIG) . . . . 32

2.2.1 DFIG model . . . . 32

2.2.1.1 Equivalent circuit and hypotheses . . . . 32

2.2.1.2 Direct, inverse, homopolar components and Fortescue transformation 33 2.2.1.3 Equations of the DFIG . . . . 34

2.2.2 DFIG control . . . . 42

2.2.2.1 Vector control . . . . 42

2.2.2.2 Other control strategies for DFIGs . . . . 46

2.3 Power converter and acquisition systems . . . . 48

2.4 Grid connection . . . . 49

2.4.1 Grid connection requirements . . . . 49

2.4.2 Grid filter design . . . . 49

2.4.2.1 Grid configuration choice . . . . 49

2.4.2.2 Design of the LCL grid filter . . . . 51

2.4.3 Hypotheses and reference frame related to the control of the grid-side converter 52 2.4.4 DC-link voltage dynamics and power balance . . . . 53

2.4.5 Control of the grid-side converter . . . . 53

2.4.5.1 Detection of the phase of the grid voltage . . . . 53

2.4.5.2 Grid-side converter start-up procedure . . . . 54

2.4.5.3 Grid-side converter control after synchronization . . . . 54

2.5 Practical realization of the 3 kW DFIG test bench . . . . 57

2.5.1 Presentation of the test bench . . . . 57

2.5.2 Implementation of the control and emulation of the turbine . . . . 59

2.6 Summary . . . . 61

3 Theoretical background on reliability management and fault detection and isola- tion 63 3.1 Introduction . . . . 63

3.2 Reliability models . . . . 64

3.2.1 The bathtub curve . . . . 64

3.2.2 Basic terminology of reliability . . . . 65

3.2.3 Principal failure distributions . . . . 66

3.2.3.1 Exponential distribution . . . . 66

3.2.3.2 Truncated normal and lognormal distributions . . . . 67

3.2.3.3 Weibull distribution . . . . 67

3.3 Ways to improve reliability . . . . 68

3.3.1 Fault prevention . . . . 69

3.3.2 Fault removal . . . . 69

3.3.3 Fault forecasting . . . . 69

3.3.4 Fault tolerance . . . . 69

3.4 Overview of fault detection and isolation (FDI) techniques . . . . 70

3.4.1 Process-history-based FDI algorithms . . . . 71

3.4.1.1 Quantitative methods . . . . 72

3.4.1.2 Qualitative methods . . . . 72

3.4.1.3 General advantages and drawbacks of process-history-based methods 72 3.4.2 Model-based FDI . . . . 72

3.4.2.1 Quantitative methods . . . . 72

3.4.2.2 Qualitative methods . . . . 73

3.4.2.3 General advantages and drawbacks of model-based methods . . . . . 73

3.4.3 Combined FDI algorithms . . . . 73

3.5 Decision functions for quantitative model-based FDI . . . . 73

3.5.1 Basic tools: incidence table and log-likelihood ratio . . . . 73

3.5.2 Elementary decision algorithms . . . . 75

3.5.3 CUSUM algorithm . . . . 75

3.5.3.1 Fault detection . . . . 75

3.5.3.2 Fault isolation . . . . 76

3.5.4 GLR algorithm . . . . 77

3.5.4.1 Fault detection . . . . 77

3.5.4.2 Fault isolation . . . . 77

3.6 Summary . . . . 78

4 Causes of failures in WTs and potential faults in DFIG drives 79 4.1 Introduction . . . . 79

4.2 Failure occurrence and costs in wind turbines . . . . 79

4.2.1 Discussion on failure rates . . . . 80

4.2.2 Consideration of the downtime relative to each failure . . . . 83

4.3 Potential faults in DFIG drives . . . . 84

4.3.1 Generator faults . . . . 84

4.3.1.1 Mechanical faults . . . . 84

4.3.1.2 Electrical faults . . . . 85

4.3.2 Power-switch faults . . . . 86

4.3.3 DC-link-capacitor faults . . . . 86

4.3.4 Sensor faults . . . . 87

4.3.4.1 General sensor-fault types . . . . 87

4.3.4.2 Current-sensor faults . . . . 88

4.3.4.3 Voltage-sensor faults . . . . 88

4.3.4.4 Position-sensor faults . . . . 88

4.3.4.5 Temperature-sensor fault . . . . 89

4.4 Summary . . . . 89

5 Fault-tolerant DFIG drives for WECS 90 5.1 Introduction . . . . 90

5.2 Fault-tolerant configurations . . . . 91

5.2.1 Fault-tolerant generator . . . . 91

5.2.2 Fault-tolerant inverter . . . . 91

5.2.3 Fault-tolerant DC-bus . . . . 92

5.2.4 Fault-tolerant measurement system . . . . 93

5.2.4.1 Position and speed measurement . . . . 93

5.2.4.2 Current measurement . . . . 93

5.2.4.3 Voltage measurement . . . . 94

5.3 Combined fault-tolerant current and position measurement using available redundancies in DFIGs . . . . 94

5.3.1 State-of-the-art of the observers and sensor-FDI techniques on DFIGs . . . . . 94

5.3.1.1 Position observers . . . . 94

5.3.1.2 Current observers . . . . 95

5.3.1.3 Voltage observers . . . . 96

5.3.1.4 Combined observers . . . . 96

5.3.1.5 FDI techniques . . . . 96

5.3.2 Combined current and position observer . . . . 97

5.3.2.1 Computation of active and reactive power transferred via the air gap 97 5.3.2.2 Magnetic saturation model . . . . 98

5.3.2.3 Estimation of the rotor position . . . . 99

5.3.2.4 Estimation of the electromagnetic torque and power . . . . 100

5.3.2.5 Stability analysis of the MRAS system . . . . 101

5.3.2.6 Estimation of the rotor currents . . . . 101

5.3.2.7 Robustness analysis . . . . 101

5.3.2.8 Behaviour of the sensorless algorithm during DFIG startup . . . . 107

5.3.3 Combined current and position FDI . . . . 110

5.3.3.1 Residual generation . . . . 111

5.3.3.2 Fault detection and isolation algorithm . . . . 111

5.3.4 Experimental validation . . . . 113

5.3.4.1 Detection of rotor-current-sensor faults . . . . 113

5.3.4.2 Detection of encoder faults . . . . 114

5.3.4.3 Detection of faults in transient state . . . . 116

5.3.4.4 Behaviour of the FDI algorithm in case of unbalanced grid voltages . 119 5.4 Summary . . . . 120

II NVH aspects of electrical drives for EVs 124 6 Principal drives for EV propulsion 125 6.1 Introduction . . . . 125

6.2 General requirements for electrical drive trains used in vehicles . . . . 126

6.3 Kinematic chains for electrical and hybrid vehicles . . . . 126

6.3.1 Kinematic chains for full-electrical vehicles . . . . 127

6.3.1.1 Single- versus multiple-motor topology . . . . 127

6.3.1.2 Variable-gear versus fixed-gear versus gearless transmission . . . . 127

6.3.1.3 System voltage . . . . 127

6.3.2 Kinematic chains for hybrid vehicles . . . . 127

6.3.2.1 Series hybrids . . . . 128

6.3.2.2 Parallel hybrids . . . . 128

6.3.2.3 Series-parallel hybrids . . . . 129

6.4 Existing technologies for energy storage . . . . 130

6.4.1 Batteries . . . . 130

6.4.2 Supercapacitors . . . . 132

6.4.3 Flywheels . . . . 132

6.4.4 Fuel cells . . . . 132

6.5 Mainly used motor types in EVs . . . . 132

6.5.1 DC motors . . . . 132

6.5.2 Induction motors . . . . 132

6.5.3 Permanent-magnet synchronous motors . . . . 133

6.5.4 Synchronous reluctance motors and permanent-magnet-assisted synchronous re- luctance motors . . . . 133

6.5.5 Switched reluctance motors . . . . 133

6.5.6 Other machine types . . . . 134

6.6 Summary . . . . 134

7 SRM drives for EVs: modelling and control 135 7.1 Introduction . . . . 135

7.2 SRM model . . . . 136

7.2.1 General working principle of SRMs . . . . 136

7.2.2 Voltage and torque equations of the SRM . . . . 138

7.2.2.1 Flux-linkage / current curves . . . . 138

7.2.2.2 Expressions of the electromagnetic torque and power . . . . 138

7.2.2.3 Interest of working with magnetic saturation . . . . 141

7.2.2.4 Voltage equation of the SRM . . . . 141

7.2.2.5 Ideal waveforms in SRMs . . . . 142

7.2.3 Identification process of SRMs . . . . 143

7.2.3.1 Computational methods . . . . 143

7.2.3.2 Experimental methods . . . . 144

7.3 SRM control . . . . 145

7.3.1 General control structure . . . . 145

7.3.2 SRM converter topologies, current control and chopping modes . . . . 146

7.3.2.1 SRM converter topologies . . . . 146

7.3.2.2 Current control techniques . . . . 146

7.3.3 Torque control strategies . . . . 150

7.3.3.1 Average torque control . . . . 150

7.3.3.2 Instantaneous torque control . . . . 152

7.4 Practical realization of the 15 kW SRM test bench . . . . 158

7.4.1 Presentation of the test bench . . . . 158

7.4.2 Implementation of the control . . . . 158

7.4.3 Simulation model of the test bench . . . . 159

7.5 Summary . . . . 160

8 Investigation of NVH aspects of SRM drives 161 8.1 Introduction . . . . 161

8.2 Main causes of NVH issues in SRMs . . . . 162

8.2.1 Principal vibration sources . . . . 162

8.2.1.1 Global mechanisms and modelling . . . . 162

8.2.1.2 Spatial distribution . . . . 163

8.2.1.3 Frequency content . . . . 163

8.2.2 Main resonance modes . . . . 164

8.2.2.1 Analytical computation of natural frequencies . . . . 165

8.2.2.2 FE computation of natural frequencies and modes . . . . 166

8.2.2.3 Experimental measurement of natural frequencies and modes . . . . . 167

8.2.2.4 Comparison of computation and measurement results on the investi- gated 8/6 SRM . . . . 168

8.2.3 Interaction between vibration sources and eigenmodes . . . . 169

8.2.4 Generation of acoustic noise . . . . 170

8.2.5 Perception of acoustic noise by the human ear and sound metrics . . . . 171

8.2.5.1 Characteristics of the human ear . . . . 171

8.2.5.2 Definition of sound metrics . . . . 172

8.3 Investigation of NVH aspects in SRMs in transient conditions . . . . 175

8.3.1 Evolution with speed . . . . 177

8.3.1.1 Reference-test results and global comments . . . . 177

8.3.1.2 Higher-speed- and coasting-test results . . . . 180

8.3.2 Evolution with torque and reference current . . . . 182

8.3.3 Comparison of soft and hard chopping . . . . 184

8.3.4 Evolution with converter DC-bus voltage . . . . 187

8.3.5 Evolution with current-hysteresis bandwidth . . . . 188

8.3.6 Evolution with hysteresis-controller sampling frequency . . . . 192

8.3.7 Influence of faults . . . . 196

8.4 Summary . . . . 197

9 Conclusion 200 9.1 Summary and contributions . . . . 200

9.1.1 Fault-tolerant DFIG drives for wind energy conversion systems . . . . 200

9.1.2 NVH aspects of electrical drives for EVs . . . . 201

9.2 Future work . . . . 202

9.2.1 Fault-tolerant DFIG drives for wind energy conversion systems . . . . 202

9.2.2 NVH aspects of electrical drives for EVs . . . . 203

References 205 Appendices 228 A Main characteristics of the DFIG-test-bench components 229 A.1 Characteristics of the rotating machines . . . . 229

A.2 Characteristics of the converters . . . . 233

A.3 Grid and filter characteristics . . . . 234

A.4 Main characteristics of the measurement chain . . . . 235

A.5 Implementation of the control on dSPACE . . . . 238

B Main characteristics of the SRM test-bench components 244 B.1 Characteristics of the rotating machines . . . . 244

B.2 Characteristics of the converters . . . . 246

B.3 Main characteristics of the measurement chain . . . . 249

B.4 Implementation of the control . . . . 252

B.5 Future upgraded version of the SRM test bench . . . . 254

C Complementary information on FDI techniques 258 C.1 Process-history-based FDI algorithms . . . . 258

C.1.1 Quantitative methods . . . . 258

C.1.1.1 Statistical methods . . . . 258

C.1.1.2 Neural networks . . . . 259

C.1.2 Qualitative methods . . . . 259

C.1.2.1 Expert systems . . . . 259

C.1.2.2 Qualitative trend analyses . . . . 259

C.2 Model-based FDI . . . . 260

C.2.1 Quantitative methods . . . . 260

C.2.1.1 Residual generation using observers . . . . 260

C.2.1.2 Residual generation using parity equations . . . . 261

C.2.1.3 Residual generation using Kalman filters . . . . 262

C.2.2 Qualitative methods . . . . 262

C.2.2.1 Causal algorithms . . . . 262

C.2.2.2 Abstraction-hierarchy-based algorithms . . . . 263

D List of publications 264 D.1 Papers published in the frame of part A of the present thesis . . . . 264

D.1.1 Conference papers . . . . 264

D.2 Papers published in the frame of part B of the present thesis . . . . 264

D.2.1 Journal paper . . . . 264

D.2.2 Conference papers . . . . 265

D.3 Additional published papers . . . . 265

D.3.1 Conference papers . . . . 265

D.4 Additional published papers (co-author) . . . . 265

D.4.1 Journal papers . . . . 265

D.4.2 Conference papers . . . . 266

ADC Analogue-to-Digital Converter.

ATC Average Torque Control.

CUSUM CUMulative SUM.

DATC Direct Average Torque Control.

DFIG Doubly-Fed Induction Generator.

DFIM Doubly-Fed Induction Machine.

DITC Direct Instantaneous Torque Control.

DPC Direct Power Control.

DTC Direct Torque Control.

EMF Electromotive Force.

ESR Equivalent Series Resistance.

EV Electrical Vehicle.

FAST Fatigue, Aerodynamics, Structures, and Turbulence (Open-source code, developed by Jason Jonkman, Ph.D. (National Wind Technology Center )).

FDI Fault Detection and Isolation.

FE Finite Element.

FEA Finite Element Analysis.

FFT Fast Fourier Transform.

FIT Failures In Time.

GHG Greenhouse Gas.

GLR Generalized Likelihood Ratio.

GOS Generalized Observer Scheme.

HALT Highly Accelerated Life Test.

HAST Highly Accelerated Stress Test.

HEV Hybrid Electrical Vehicle.

HF High Frequency.

xv

ICE Internal Combustion Engine.

IGBT Insulated Gate Bipolar Transistor.

IM Induction Machine.

JFU Jordan, Frone and Uner.

LED Light-Emitting Diode.

LLR Log-Likelihood Ratio.

LUT Look-Up Table.

MDT Mean Down Time.

MPP Maximum Power Point.

MPPT Maximum Power Point Tracking.

MRAS Model-Reference Adaptive System.

MTBF Mean Time Between Failures.

MTTF Mean Time To Failure.

MTTR Mean Time To Repair.

MUT Mean Up Time.

NEDC New European Driving Cycle.

NVH Noise, Vibration and Harshness.

PCC Point of Common Coupling.

PHEV Plug-in Hybrid Electrical Vehicle.

PI Proportional-Integral.

PLL Phase Locked Loop.

PM Permanent Magnet.

PMaSynRM Permanent-Magnet-assisted Synchronous Reluctance Machine.

PMSG Permanent-Magnet Synchronous Generator.

PMSM Permanent-Magnet Synchronous Machine.

PSD Power Spectral Density.

PWM Pulse Width Modulation.

rms root-mean-square.

RY Roark and Young.

SCIG Squirrel-Cage Induction Generator.

SCIM Squirrel-Cage Induction Machine.

SIL Sound Intensity Level.

SPL Sound Pressure Level.

SRM Switched Reluctance Machine.

SynRM Synchronous Reluctance Machine.

TSF Torque Sharing Function.

TSR Tip Speed Ratio.

UIO Unknown Input Observer.

VSI Voltage Source Inverter.

WECS Wind Energy Conversion Systems.

WRIG Wound-Rotor Induction Generator.

WRIM Wound-Rotor Induction Machine.

WRSG Wound-Rotor Synchronous Generator.

WRSM Wound-Rotor Synchronous Machine.

WT Wind Turbine.

ZOH Zero-Order Hold.

v

Wind speed [m/s] . . . . 16

β

Shape parameter of the Weibull distribution . . . . 16

η

Scale parameter of the Weibull distribution . . . . 16

γ

Location parameter of the Weibull distribution . . . . 16

f Frequency [Hz] . . . . 18

Ω

Rotational speed of the blades [rad

/s] . . . . 19

ρ

Density of air [kg/m

] . . . . 19

β Pitch angle [rad] . . . . 20

c

Power coefficient of a wind turbine . . . . 21

λ Tip speed ratio (TSR) . . . . 22

c

Torque coefficient of a wind turbine . . . . 22

n Turns ratio . . . . 32

γ Slip . . . . 32

p Number of pole pairs . . . . 32

ω

Synchronous pulsation [rad

/s] . . . . 32

f

Stator frequency (DFIG) . . . . 32

Ω Rotational speed of the machine [rad

/s] . . . . 32

v

= v

v

v

Vector of stator voltages and its components in the abc frame [V] 32 v

= v

v

v

Vector of rotor voltages and its components in the abc frame [V] 33 e

= e

e

e

= e

Vector of magnetizing branch voltages and its components in the abc frame [V] . . . . 33

i

= i

i

i

Vector of stator currents and its components in the abc frame [A] 33 i

= i

i

i

Vector of rotor currents and its components in the abc frame [A] 33 i

= i

i

i

Vector of magnetizing currents and its components in the abc frame [A] . . . . 33

R

Stator resistance [Ω] . . . . 33

R

Rotor resistance [Ω] . . . . 33

R

Resistance modelling the iron losses [Ω] . . . . 33

L

Magnetization inductance [H] . . . . 33

L

Stator leakage inductance [H] . . . . 33

L

Rotor leakage inductance [H] . . . . 33

t Time [s] . . . . 33

θ

Rotor position (i.e. electrical angle between the phases a of the stator and of the rotor) [rad

/s] . . . . 35

T

Electromagnetic power [W] . . . . 35

T

Clarke’s transformation . . . . 36

xviii

P

Active power flowing entering the machine at the stator termi-

nals [W] . . . . 37

Q

Reactive power flowing entering the machine at the stator ter- minals [VAr] . . . . 37

P

Active power flowing coming out of the machine at the rotor terminals [W] . . . . 37

Q

Reactive power flowing coming out of the machine at the rotor terminals [W] . . . . 37

θ

Electrical angle between the superposed α

and a axes of the stator and the q axis [rad

] . . . . 37

P

Electromagnetic power [W] . . . . 41

P

Stator Joule losses [W] . . . . 41

P

Rotor Joule losses [W] . . . . 41

P

Iron losses [W] . . . . 41

σ Leakage coefficient . . . . 45

T

Sampling time [S] . . . . 46

T

Switching period [s] . . . . 49

L

Grid inductance [H] . . . . 49

R

Grid resistance [Ω] . . . . 49

C

DC-link capacitance [F] . . . . 50

L

Converter-side LCL-filter inductance [H] . . . . 50

L

Grid-side LCL-filter inductance [H] . . . . 50

C

LCL-filter capacitance [H] . . . . 50

R

LCL-filter damping resistance [Ω] . . . . 50

L

Grid inductance [H] . . . . 50

v

DC-bus voltage [V] . . . . 50

f

Switching frequency of the grid-side converter [Hz] . . . . 51

ω

Grid pulsation [rad

/s] . . . . 55

λ

(t) Failure rate . . . . 65

r(k) Vector of residuals . . . . 73

p

(r(k)) Probability density of the residuals of a healthy system . . . . 73

p

(r(k)) Probability density of the residuals of a system in case fault i has occurred . . . . 73

H

Hypothesis considering that no fault has occurred . . . . 74

H

Hypothesis considering that fault i has occurred . . . . 74

s

(k) Log-likelihood ratio between hypotheses H

and H

. . . . 74

µ

Mean vector associated with p

(r(k)) . . . . 75

Σ Variance matrix associated with p

(r(k)) . . . . 75

µ

Mean vector associated with p

(r(k)) . . . . 75

S

(k) Cumulative sum of the LLR between hypotheses H

and H

. 75 g

(k) Decision function corresponding to fault i . . . . 75

h

Detection threshold . . . . 76

h

Isolation threshold . . . . 76

∆t

Mean detection delay corresponding to fault i . . . . 76

κ

Kullback-Leibler information . . . . 76

h Detection and isolation threshold . . . . 76

g

(k) Isolation function corresponding to fault i . . . . 76

P

Active power crossing the air gap (computed using the reference

part of the MRAS [W]) . . . . 97

Q

Reactive power crossing the air gap (computed using the refer-

ence part of the MRAS [VAr]) . . . . 97

Q

Reactive power consumed in the magnetizing inductance [VAr] 98 P ˆ

Active power crossing the air gap (computed using the adaptive part of the MRAS [W]) . . . . 98

Q ˆ

Reactive power crossing the air gap (computed using the adap- tive part of the MRAS [VAr]) . . . . 98

S

Apparent power transferred via the air gap [VA] . . . . 99

Ω ˆ Estimated rotor speed [rad

/s] . . . . 100

θ ˆ

Estimated rotor position [rad

] . . . . 100

∆θ

Difference between estimated and actual rotor angles [

] . . 102

N

Amount of stator poles (SRM) . . . . 136

N

Amount of rotor poles (SRM) . . . . 136

m Number of phases (SRM) . . . . 136

N

Multiplication factor (SRM) . . . . 136

S Number of strokes per revolution . . . . 137

Stroke angle . . . . 137

f

Fundamental frequency in the phases of the SRM . . . . 137

f

Number of strokes per second . . . . 137

ψ

Flux linkage of one phase of the SRM [Vs] . . . . 139

i

SRM phase current [A] . . . . 139

L

(θ

) SRM phase inductance [H] . . . . 139

W

Accumulated magnetical co-energy in an SRM phase [W] . . . 140

ω Rotor angular speed [rad

/s] . . . . 140

v

SRM phase voltage [V] . . . . 141

R

SRM phase resistance [Ω] . . . . 141

e

SRM pseudo-EMF [V] . . . . 141

m

Circumferential mode order . . . . 164

m

Longitudinal mode number . . . . 164

R

Stator yoke average radius [m] . . . . 165

h

Stator yoke thickness [m] . . . . 165

E Young modulus of iron . . . . 165

ρ Density of iron . . . . 165

ν Poisson coefficient . . . . 166

1 Evolution of the total gross electricity production in EU-28 in 1990, 2000 and 2013, based on data from [1]. . . . 3 2 Evolution of global installed wind capacity between 2000 and 2015 and previsions up

to 2020 using data from [2, 3]. . . . 4 4 Evolution of global installed offshore wind capacity between 2000 and 2015 based on

data from [2, 3]. . . . 5 5 Integration of the different objectives of the POWER project into one global system. . 6 6 Evolution of the consumption and of the CO

emissions of passenger cars in the EU

from 2001 to 2014, according to the data from [4]. . . . . 7 7 Repartition of the sold / registered cars per fuel from 2001 to 2014, according to the

data from [4]. . . . 8 8 Logo of the DeMoTest-EV project. . . . 8 1.1 Principal components of a wind turbine [5]. . . . 15 1.2 Simulated dissipation of boundary layer wind over the whole planet (a) and over non-

glaciated land (b) obtained with a general circulation model (T42 spectral resolution (2.8

longitude by 2.8

latitude) and 10 vertical layers) [6]. . . . 16 1.3 Wind spectrum model of van der Hoven [7]. . . . 17 1.4 Evolution of the air flow crossing the rotor of a horizontal-axis WT (in blue). The area

swept by the rotor is a disc of surface S. In red dashed lines, the air flow that would have crossed surface S if there was no turbine. . . . 20 1.5 Components of the relative wind speed and aerodynamic forces acting on a blade. . . 20 1.6 Evolution of c

coefficients for different types of windmills in function of the TSR (λ),

compared to the theoretical Betz (upper dashed line) and Glauert (upper asymptotical curve) limits [8]. . . . . 22 1.7 Evolution of c

(a) and of the produced power (b) of a fixed-speed WT in function of

wind speed [9]. The solid and dashed lines correspond to passive-stall and active-stall controls respectively. . . . 23 1.8 Evolution of the rotor speed and the produced power of a variable-speed WT in function

of wind speed [10]. . . . 24 1.9 Principle of optimal TSR and torque MPPT methods [11]. The optimal power curve is

supposed to be known and is used as a reference for the control of the generator. . . . 24 1.10 Principle of the ‘P&O’ observer [11]. Speed steps are applied on a regular basis and the

evolution of output power is measured. The sense of the speed step is changed in case of negative power variation. . . . 25 1.11 Electrical scheme of a WT using a SCIG. . . . . 26 1.12 Electrical scheme of a WT using a WRIG. . . . 26 1.13 Electrical scheme of a WT using a DFIG. . . . 27 1.14 Electrical scheme of a WT using a WRSG. . . . . 28 1.15 Electrical scheme of a WT using a PMSG. . . . 29

xxi

2.1 Equivalent scheme of the DFIG (Π scheme). . . . 32 2.2 General structure of an RST controller. . . . . 46 2.3 Phase-to-neutral equivalent circuit of the DC-bus, grid-side converter, RL filter and grid. 50 2.4 Phase-to-neutral equivalent circuit of the DC-bus, grid-side converter, LCL filter and

grid (the parasitic grid and filter resistances and the discharging resistance of the DC- bus have been neglected). . . . 51 2.5 Schematic of a basic PLL. . . . 53 2.6 3-phase synchronous reference frame PLL (SRF-PLL). . . . 54 2.7 Small-signal model of a PLL. . . . 54 2.8 Small-signal model of the DC-bus voltage control loop, including the disturbance trans-

fer functions and feed-forward actions (in red and green respectively) related to the current ˆ I

injected or taken by the rotor-side converter and the grid voltage ˆ V

. . . . 56 2.9 3 kW test bench consisting in (from left to right) a wound-rotor synchronous machine,

a wound-rotor induction machine (used as a DFIG) and a poly-excitation DC machine (used as a DC machine with independent excitation to emulate the turbine). A tacho- generator and an encoder are mounted on the left, while a torquemeter is mounted between the DFIG and the DC machine. . . . 57 2.10 Evolution of the magnetization inductance L

with respect to the magnitude of the

magnetization branch voltage E

(synchronous speed; open rotor winding). . . . . 57 2.11 Electrical circuit of the 3 kW DFIG test bench. . . . 58 2.12 Simplified schematic of the control structure of the DFIG. The orange and green blocks

are used for the synchronization and production modes respectively. . . . 60 2.13 Simplified schematic of the control structure of the grid-side converter. . . . 60 2.14 Experimental results on the 3 kW DFIG test bench for an emulated average wind of 7

m/s using an MPPT algorithm based on the model of the turbine. From top to bottom : wind speed, rotational speed of the turbine and the ratio TSR/TSRopt. . . . 62 2.15 Experimental results on the 3 kW DFIG test bench for an emulated average wind of

7 m/s. From top to bottom : electromagnetic and measured torque, and active and reactive power (motor convention). . . . 62 3.1 Classical evolution of failure rates of devices with time, known as the ‘bathtub’ curve. 64 3.2 Influence of the shape parameter β

and scale parameter γ

on the Weibull probability

density and cumulative distribution functions. . . . 68 3.3 Classification of diagnostic methods proposed in [12]. . . . 71 4.1 Comparison of failure rates in function of failure type combining references [13] (Ri-

brant), [14] (Spinato), [15] (Stenberg) and [16] (Carroll). The “Other” category includes but is not limited to structure, sensors, converter and control system. . . . 81 4.2 Comparison of average failure rates presented in references [13] (Ribrant) , [14] (Spinato),

[15] (Stenberg) and [16] (Carroll). . . . 81 4.3 Comparison of failure distributions presented in references [13] (Ribrant), [14] (Spinato),

[15] (Stenberg) and [16] (Carroll). . . . 82 4.4 Evolution of failure distributions with generator rated power [17]. The data are based

on reports of 1200 repair operations of Shermco Industries between 2005 and 2010. . . 83 4.5 Comparison of downtime distributions presented in references [13] (Ribrant) , [14]

(Spinato) and [15] (Stenberg). . . . 84 4.6 Typical measurement chain based on [18, 19]. This chain can contain an internal closed

loop with an actuator for linearization purpose. In some cases, not all components are

present. . . . 87

5.1 Fault-tolerant inverter topology using a spare leg (or the middle point of the DC bus) [20]. 92

5.2 Fault-tolerant back-to-back converter for WECS applications using a spare leg, which can be used either for the grid or the converter side [20]. . . . 93 5.3 Location on the DFIG equivalent circuit of the computed values of air-gap active and

reactive power. . . . 98 5.4 Space-vector diagram considering an estimation error ∆θ

on the rotor position. The

ˆ

α

β ˆ

rotor reference frame, the space vector ~ ˆ ı

and the related angles ˆ ϕ

and ˆ θ

, repre- sented in blue, correspond to the estimations of the adaptive model. . . . 99 5.5 General schematic of the mechanical-state estimator. . . . 100 5.6 Simplified observer dynamics considering the PI controller, the integrator and the ap-

proximation of the sine function in the vicinity of the equilibrium points (∆θ

= 0 (a) and ∆θ

= π (b)). . . . . 101 5.7 Simulated evolution of |∆θ

| with the amplitude of the rotor current I

for different load

torques and reference speeds (point with coordinates (0.3798 A, 39.88

) @ 0 Nm, 1200 rpm, fixed L

omitted for clarity reasons; plotted values corresponding to 0.1 s-time averages). The indication ‘fixed L

’ in the caption refers to the results obtained with the simplified estimator considering L

as constant and equal to its rated value L

defined in Table A.2. . . . 102 5.8 Experimental evolution of |∆θ

| with the amplitude of the rotor current I

for different

measured shaft torques and reference speeds (plotted values corresponding to 0.1 s-time averages). The indication ‘fixed L

’ in the caption refers to the results obtained with the simplified estimator considering L

as constant and equal to its rated value L

defined in Table A.2. . . . 103 5.9 Difference between 1 s-averaged rotor-angle-estimation errors with and without mises-

timation of parameters R

and L

: simulation results (± 10 % and ± 20 % errors are considered, as well as a unitary power factor at the stator terminals and a speed of 1200 rpm). . . . . 104 5.10 Difference between 1 s-averaged rotor-angle-estimation errors with and without mises-

timation of parameters R

and L

: experimental results (± 10 % and ± 20 % errors are considered, as well as a unitary power factor at the stator terminals and a speed of 1200 rpm). . . . . 104 5.11 Difference between 1 s-averaged rotor-angle-estimation errors (top) and standard de-

viation of the rotor-angle-estimation error (bottom) with and without grid unbalance:

simulation results (5, 10 and 20 % reduction and 5 % increase of one grid phase voltage are considered, as well as a unitary power factor at the stator terminals and a speed of 1200 rpm). . . . . 105 5.12 Difference between 1 s-averaged rotor-angle-estimation errors (top) and standard de-

viation of the rotor-angle-estimation error (bottom) with and without grid unbalance:

experimental results (5, 10 and 20 % reduction and 5 % increase of one grid phase voltage are considered, as well as a unitary power factor at the stator terminals and a speed of 1200 rpm). . . . . 105 5.13 From top to bottom (simulation results): difference between estimated and actual ro-

tor angles, values of estimated and actual speeds, electromagnetic torques and power (unitary power factor at the stator terminals; 1200 rpm; -4 Nm electromagnetic torque;

20 % reduction of the voltage of phase c). . . . 106 5.14 From top to bottom (experimental results): difference between estimated and measured

rotor angles, values of estimated and measured speeds, estimated (electromagnetic) and

measured (shaft) torques and estimated electromagnetic power (unitary power factor

at the stator terminals; 1200 rpm; -4 Nm shaft torque; 20 % reduction of the voltage

of phase c). . . . 107

5.15 From top to bottom (simulation results): difference between estimated and actual ro- tor angles, values of estimated and actual speeds, electromagnetic torques and power (unitary power factor at the stator terminals; speed reference step from 1200 to 1600 rpm at 7 s; -8 Nm steady-state shaft torque). . . . 108 5.16 From top to bottom (experimental results): difference between estimated and measured

rotor angles, values of estimated and measured speeds, estimated (electromagnetic) and measured torques and estimated electromagnetic power (unitary power factor at the stator terminals; speed reference step from 1200 to 1600 rpm at 0.9 s; -8 Nm steady- state shaft torque). . . . 108 5.17 Open-loop starting behaviour of the sensorless algorithm during the synchronization of

the stator of the DFIG with the grid – experimental results. The machine is driven at 1000 rpm and the braking-torque reference is set to 5 Nm after synchronization. At time equal to 123.600 s the converter starts to inject currents in the rotor and the stator contactor closes at 127.14 s. . . . 109 5.18 Closed-loop starting behaviour of the sensorless algorithm during the synchronization

of the stator of the DFIG with the grid – experimental results. The machine is driven at 1000 rpm and the braking-torque reference is set to 5 Nm after synchronization. At time equal to 168.72 s the converter starts to inject currents in the rotor and the stator contactor closes at 170.73 s. . . . 110 5.19 Schematic of the CUSUM-based fault-detection algorithm. . . . 112 5.20 Detection of an offset at the output of a current sensor (occurring at 109.467 s and

detected at 109.535 s) at -4.5 Nm and 1200 rpm – experimental results. . . . 114 5.21 Detection of a current-sensor gain change (occurring at 70.383 s and detected at 70.397

s) at -4.5 Nm and 1550 rpm – experimental results. . . . 115 5.22 Detection of an encoder fault (occurring at 171.233 s and detected at 171.256 s) at -8

Nm and 1200 rpm – experimental results. . . . 115 5.23 Detection of an encoder fault (occurring at 164.642 s and detected at 164.644 s) at -4.5

Nm and 1200 rpm – experimental results. . . . 116 5.24 Behaviour of the algorithm during a speed transient (in this case a speed ramp of

400 rpm/s is imposed on the reference of the DC machine at 157.8 s) without fault – experimental results. . . . 117 5.25 Behaviour of the algorithm during a torque transient (in this case a torque-reference

step of -8 Nm is imposed on the DFIG at 154.025 s) without fault – experimental results.117 5.26 Detection of a current-sensor outage (occurring at 147.323 s and detected at 147.325 s)

just after a torque-reference step from -4.5 Nm to -10 Nm (at 147.225 s) at 1550 rpm – experimental results. . . . 118 5.27 Detection of a current-sensor outage (occurring at 147.323 s and detected at 147.325 s)

just after a torque-reference step from -4.5 Nm to -10 Nm (at 147.225 s) at 1550 rpm:

detail of the behaviour near the fault occurrence and isolation – experimental results. 118 5.28 Spurious detection of a current-sensor and encoder faults during the DFIG start-up

process presented in Figure 5.17 (using the measured position) – experimental results. 119 5.29 Detection of a current-sensor outage (occurring at 52.407 s and detected at 52.409 s)

at 1200 rpm for a shaft torque of -4 Nm with grid-voltage unbalance (the voltage on phase c is reduced by 10 % compared to the other phases) – experimental results. . . . 120 5.30 Detection of an encoder outage (occurring at 103.973 s and detected at 103.976 s) at

1200 rpm for a shaft torque of -4 Nm with grid-voltage unbalance (the voltage on phase

c is reduced by 10 % compared to the other phases) – experimental results. . . . 121

6.1 Global schematic of the drive train of a series-hybrid car. . . . . 128

6.2 Global schematic of the drive train of a parallel-hybrid car. . . . 129

6.3 Global schematic of the drive train of a series-parallel-hybrid car. . . . 130

6.4 Specific power in function of specific energy at cell level for different battery technologies [21]. . . . 131 7.1 Schematic of the cross-section of an 8/6 SRM, i.e. having eight salient poles on the

stator associated with concentrated coils (4 phases) and six soft-iron salient poles on the rotor. . . . 136 7.2 Cross-section of the investigated 15 kW 8/6 SRM and flux lines for one excited phase

(left: unaligned position; right: aligned position) [22]. . . . . 137 7.3 Flux-linkage-vs.-current curves of the investigated 8/6 SRM for various rotor positions

(in mechanical degrees), obtained by FEA. The cyan and the blue curves correspond to the unaligned and to the aligned positions (cf. Figure 7.2 left and right) respectively. 138 7.4 Calculation of the electromagnetic torque. . . . 139 7.5 Flux-linkage trajectory in real conditions. The area inside the cycle corresponds to the

produced mechanical work W

. . . . 140 7.6 Equivalent circuit of the SRM, including iron losses, inspired from [23]. . . . 142 7.7 Idealized and real waveforms (in blue and black respectively) of phase inductance, flux

linkage, current, voltage and torque in function of the rotor position in electrical degrees θ

, considering a linear variation of the inductance as soon as the poles start overlapping.143 7.8 General schematic of the control structure of an SRM. . . . 146 7.9 Typical converter structure for an 8/6 SRM, with one asymmetrical H-bridge per phase. 146 7.10 Torque-speed curve of the SRM, including constant-torque, constant-power and falling-

power regions for both motoring and generating modes [24]. . . . 147 7.11 Possible conduction modes of one phase of the SRM. Depending on the states of both

power switches T1 and T2 of the asymmetric H-bridge, the applied voltage on the SRM phase may be +V

, 0 or −V

(cases a, b and c respectively). . . . 148 7.12 Simulated flux linkage, current and torque waveforms corresponding to one phase of the

investigated 8/6 SRM with 10 Nm load for different rotational speeds (in hard-chopping mode). The -30

and 0

positions correspond to the unaligned and aligned position, i.e. to the geometry of Figure 7.2a and b and to the cyan and blue curves of Figure 7.3 respectively. . . . 149 7.13 Simulated flux linkage, current and torque waveforms corresponding to one phase of the

investigated 8/6 SRM with 10 Nm load for different rotational speeds (in soft-chopping mode). The -30

and 0

positions correspond to the unaligned and aligned position, i.e. to the geometry of Figure 7.2a and b and to the cyan and blue curves of Figure 7.3 respectively. . . . 149 7.14 General structure of ATC on a 4-phase machine. . . . 151 7.15 General structure of DATC on a 4-phase machine. . . . 151 7.16 General structure of current-profiling control on a 4-phase machine. . . . . 153 7.17 Comparison of the different torque-sharing functions. . . . 154 7.18 Piecewise-cubic torque-sharing function [25]. . . . 155 7.19 General structure of DITC on a 4-phase machine, using an estimation of the torque

based on phase-current and rotor-position measurements. . . . 156 7.20 Voltage and torque waveforms in the case of DITC [22]. . . . 156 7.21 Space vector construction and components in the stationary αβ frame in the case of a

4-phase machine. . . . 157 7.22 Main electrical circuit of the SRM test bench. The whole bench (SRM and DC machine)

is controlled using 1103 dSPACE fast-prototyping hardware, to interface the machines,

converters and sensors with the control designed in Matlab/Simulink environment. . . 158

7.23 SRM test-bench control structure. The selection of the excited phase and the generation of the current reference are implemented in Simulink, while an external microcontroller performs the hysteresis control at a higher sampling rate. The T1 and T2 inputs of the Inverter + SRM block refer to the control inputs of the upper and lower power switches in Figure 7.11. . . . . 159 7.24 Test bench model in AMESim software with ATC control in soft-chopping mode. . . . 160 8.1 Circumferential modes of a cylindrical shell [26]. . . . . 165 8.2 Ovalization, triangular and square modes without torsion (top) and with torsion (bot-

tom) [27]. . . . 165 8.3 Model of the stator as a cylindrical shell for analytical computation of the natural

frequencies. . . . 166 8.4 Main vibration modes shapes of the investigated SRM obtained by FE computations

in [22]. . . . 167 8.5 Ovalization mode with torsion (mode 2-1) (left) and mode 2-2 (right) of the investigated

SRM obtained by FE computation in [28]. . . . 168 8.6 Deflection for a 1 N/m

radial force wave (ζ = 0.01) in the investigated 8/6 SRM. . . 171 8.7 Radiated acoustic noise level for a 1 N/m

radial force wave (ζ = 0.01) in the investi-

gated 8/6 SRM. . . . 171 8.8 Deflection for a 1 N/m

radial force wave (ζ = 0.2) in the investigated 8/6 SRM. . . . 172 8.9 Radiated acoustic noise level for a 1 N/m

radial force wave (ζ = 0.2) in the investigated

8/6 SRM. . . . 172 8.10 Frequency and intensity ranges of the hearing area, including the thresholds of hearing

and of pain (for pure tones in steady-state condition, i.e. for a duration longer than 0.1 s), the limit of damage risk and the speech and music areas [29]. . . . . 173 8.11 Fletcher and Munson curves: loudness levels [phons] in function of sound pressure level

[dB] and frequency [Hz] [30]. The dotted curve corresponds to the threshold of hearing.

The threshold of pain is situated between the 110 phon and 120 phon curves. . . . 173 8.12 Example of loudness computation by noting the specific loudnesses per third of octave

and the associated masking effects in a dedicated chart [29]. . . . 175 8.13 Weighting function g

(z) for the computation of the sharpness. . . . 175 8.14 Principal components of the experimental setup. The sensors considered in the pre-

sented results are circled in yellow. . . . 176 8.15 Phase current, radial vibration and acoustic noise frequency content (speed-ramp test,

soft chopping, no-load condition). The 0 dB references are 1 m/s

, 1 A and 20 µPa respectively. . . . 178 8.16 Evolution of loudness and sharpness for no-load, 5 and 10 Nm load speed-ramp tests

with soft chopping. . . . 179 8.17 Phase current, radial vibration and acoustic noise frequency content (speed-ramp test,

soft chopping, with 5 Nm load). The 0 dB references are 1 m/s

, 1 A and 20 µPa respectively. . . . 180 8.18 Comparison of the FFTs of the phase current, radial vibration and acoustic noise at

1000 rpm for no-load, 5 and 10 Nm load and coasting tests using soft chopping. . . . . 180 8.19 Phase current, radial vibration and acoustic noise frequency content (speed-ramp test

(50 rpm/s) up to 4000 rpm, soft chopping, no-load condition). The 0 dB references are 1 m/s

, 1 A and 20 µPa respectively. . . . 181 8.20 Evolution of loudness and sharpness up to 4000 rpm for soft and hard chopping (un-

loaded) speed-ramp and coasting tests. . . . 182 8.21 Phase current, radial vibration and acoustic noise frequency content (coasting test from

4000 rpm, no-load condition). The 0 dB references are 1 m/s

, 1 A and 20 µPa respectively.182