Renewable Energy Systems: Modelling, Optimization and Control

Renewable Energy Systems: Modelling, Optimization and Control aims to cross-pollinate recent advances in the study of renewable energy control systems by bringing together diverse scientific breakthroughs on the modeling, control and optimization of renewable energy systems by leading researchers. The book brings together the most comprehensive collection of modeling, control theorems and optimization techniques to help solve many scientific issues for researchers in renewable energy and control engineering. Many multidisciplinary applications are discussed, including new fundamentals, modeling, analysis, design, realization and experimental results. The book also covers new circuits and systems to help researchers solve many nonlinear problems.

This book fills the gaps between different interdisciplinary applications, ranging from mathematical concepts, modeling, and analysis, up to the realization and experimental work.

• Covers modeling, control theorems and optimization techniques which will solve many scientific issues for researchers in renewable energy
• Discusses many multidisciplinary applications with new fundamentals, modeling, analysis, design, realization and experimental results
• Includes new circuits and systems, helping researchers solve many nonlinear problems

• Language: English
• Publisher: Academic Press
• Release date: Sep 9, 2021
• ISBN: 9780128203989

Book preview

Renewable Energy Systems

Modeling, Optimization and Control

Edited by

Ahmad Taher Azar

Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

Nashwa Ahmad Kamal

Faculty of Engineering, Cairo University, Giza, Egypt

International Group of Control Systems (IGCS), Cairo, Egypt

Table of Contents

Cover image

Title page

Copyright

List of contributors

Preface

About the book

Objectives of the book

Organization of the book

Book features

Audience

Acknowledgments

Chapter 1. Efficiency maximization of wind turbines using data-driven Model-Free Adaptive Control

Abstract

1.1 Introduction

1.2 Problem statement

1.3 Control design

1.4 Simulation study using FAST

1.5 Conclusions

References

Chapter 2. Advanced control design based on sliding modes technique for power extraction maximization in variable speed wind turbine

Abstract

2.1 Introduction

2.2 Modeling variable speed wind turbine

2.3 Sliding mode control design

2.4 Simulation results

2.5 Conclusion and future directions

Acknowledgments

Nomenclature

References

Appendix

Chapter 3. Generic modeling and control of wind turbines following IEC 61400-27-1

Abstract

3.1 Introduction

3.2 Literature review

3.3 Modeling, simulation and validation of the Type 3 WT model defined by Standard IEC 61400-27-1

3.4 Model validation results

3.5 Conclusions

References

Chapter 4. Development of a nonlinear backstepping approach of grid-connected permanent magnet synchronous generator wind farm structure

Abstract

4.1 Introduction

4.2 Related work

4.3 Mathematical model of wind turbine generator

4.4 Control schemes of wind farm

4.5 Simulation result analysis

4.6 Conclusions

Appendix

References

Further reading

Chapter 5. Model predictive control-based energy management strategy for grid-connected residential photovoltaic–wind–battery system

Abstract

5.1 Introduction

5.2 Related works

5.3 The architecture of original grid-tied PV–WT–battery and optimal control strategy

5.4 Energy management strategy and the model of the open-loop control

5.5 Model predictive control for the PV/wind turbine/battery system

5.6 Results and discussion

5.7 Conclusion

References

Chapter 6. Efficient maximum power point tracking in fuel cell using the fractional-order PID controller

Abstract

6.1 Introduction

6.2 PEMFC system description

6.3 MPPT control configuration

6.4 Design and implementation of FOPID MPPT control technique

6.5 Controller tuning using GWO

6.6 MPPT performance analysis

6.7 Conclusion

References

Chapter 7. Robust adaptive nonlinear controller of wind energy conversion system based on permanent magnet synchronous generator

Abstract

7.1 Introduction

7.2 Speed-reference optimization: power to optimal speed

7.3 Modeling of the association permanent magnet synchronous generator–AC/DC/AC converter

7.4 State-feedback nonlinear controller design

7.5 Output-feedback nonlinear controller design

7.6 Digital implementation

7.7 Conclusion

References

Chapter 8. Improvement of fuel cell MPPT performance with a fuzzy logic controller

Abstract

8.1 Introduction

8.2 Modeling of proton-exchange membrane fuel cells

8.3 Mathematical model of DC–DC converter

8.4 Proposed algorithm

8.5 Results and analysis

8.6 Discussion

8.7 Conclusion and perspectives

References

Chapter 9. Control strategies of wind energy conversion system-based doubly fed induction generator

Abstract

9.1 Introduction

9.2 Modeling with syntheses of PI controllers of wind system elements

9.3 Results and discussions

9.4 Conclusion

Appendix

References

Chapter 10. Modeling of a high-performance three-phase voltage-source boost inverter with the implementation of closed-loop control

Abstract

10.1 Introduction

10.2 Mathematical analysis of the three-phase boost inverter

10.3 System description

10.4 Results and discussions

10.5 Conclusion

References

Chapter 11. Advanced control of PMSG-based wind energy conversion system applying linear matrix inequality approach

Abstract

11.1 Introduction

11.2 Recent research on control in wind energy conversion systems

11.3 Model of the PMSG-based WECS

11.4 Controller design of the PMSG-based WECS

11.5 Simulation results and discussion

11.6 Conclusion

Appendix

References

Chapter 12. Fractional-order controller design and implementation for maximum power point tracking in photovoltaic panels

Abstract

12.1 Introduction

12.2 Related work

12.3 Problem formulation

12.4 Fractional-order design techniques for MPPT of photovoltaic panels

12.5 Numerical experiments

12.6 Discussion

12.7 Conclusion

References

Chapter 13. Techno-economic modeling of stand-alone and hybrid renewable energy systems for thermal applications in isolated areas

Abstract

13.1 Introduction

13.2 Materials and methods

13.3 Results and discussions

13.4 Technoeconomic analysis of the hybrid energy-based cooling system

13.5 Sensitivity analysis

13.6 Conclusion

References

Chapter 14. Solar thermal system—an insight into parabolic trough solar collector and its modeling

Abstract

14.1 Introduction

14.2 Related work

14.3 Parabolic trough solar collector—history

14.4 Parabolic trough solar collector—an overview

14.5 Performance evaluation of PTSC

14.6 Analytical thermal models

14.7 1-D heat transfer model

14.8 Potential applications

14.9 Discussion

14.10 Conclusion

Nomenclature

References

Chapter 15. Energy hub: modeling, control, and optimization

Abstract

15.1 Introduction

15.2 Energy management systems

15.3 Concept of energy hub

15.4 Mathematical modeling of energy hub

15.5 Energy hub with storage capacities

15.6 Integration of renewable resources to energy hub

15.7 Simulations

15.8 Optimization of energy hub in GAMS

15.9 Conclusion

References

Chapter 16. Simulation of solar-powered desiccant-assisted cooling in hot and humid climates

Abstract

16.1 Introduction

16.2 Literature survey

16.3 System description

16.4 Measurements

16.5 Data reduction and uncertainty analysis

16.6 Results and discussion

16.7 Prediction of system performance by use of TRNSYS simulation

16.8 Conclusion

Nomenclature

References

Chapter 17. Recent optimal power flow algorithms

Abstract

17.1 Introduction

17.2 Moth-flame optimization technique

17.3 Moth swarm algorithm

17.4 Multiverse optimization

17.5 Wale optimization algorithm

17.6 Objective functions

17.7 Results and discussions

17.8 Conclusion

Appendix A (–)

References

Chapter 18. Challenges for the optimum penetration of photovoltaic systems

Abstract

Nomenclature

18.1 Introduction

18.2 PV system management

18.3 PV system grid connection

18.4 Future technical regulatory aspects

18.5 Conclusions

Acknowledgments

References

Chapter 19. Modeling and optimization of performance of a straight bladed H-Darrieus vertical-axis wind turbine in low wind speed condition: a hybrid multicriteria decision-making approach

Abstract

19.1 Introduction

19.2 Related work

19.3 Turbine design and experimental description

19.4 Integrated entropy–multicriteria ratio analysis method

19.5 Modeling of vertical-axis wind turbine using integrated entropy–multicriteria ratio analysis method

19.6 Results and discussion

19.7 Conclusions and scope for future work

References

Chapter 20. Maximum power point tracking design using particle swarm optimization algorithm for wind energy conversion system connected to the grid

Abstract

20.1 Introduction

20.2 Wind energy conversion system modeling

20.3 Control strategies of the maximum power point tracking

20.4 Field-oriented control technique of the active and reactive power

20.5 Simulation results and discussion

20.6 Conclusion

Appendix A

References

Chapter 21. Multiobjective optimization-based energy management system considering renewable energy, energy storage systems, and electric vehicles

Abstract

21.1 Introduction

21.2 System description

21.3 Proposed scheduling and optimization model

21.4 Results and discussion

21.5 Conclusion

References

Chapter 22. Fuel cell parameters estimation using optimization techniques

Abstract

22.1 Introduction

22.2 Mathematical model of proton exchange membrane fuel cell stacks

22.3 Optimization techniques

22.4 Case study

22.5 Results and discussion

22.6 Conclusion

References

Chapter 23. Optimal allocation of distributed generation/shunt capacitor using hybrid analytical/metaheuristic techniques

Abstract

23.1 Introduction

23.2 Objective function

23.3 Mathematical formulation of the analytical technique

23.4 Metaheuristic technique

23.5 Simulation results

23.6 Conclusion

References

Chapter 24. Optimal appliance management system with renewable energy integration for smart homes

Abstract

24.1 Introduction

24.2 Related work

24.3 System architecture

24.4 The proposed approach for scheduling the home appliances

24.5 Results and discussion

24.6 Conclusion

References

Chapter 25. Solar cell parameter extraction using the Yellow Saddle Goatfish Algorithm

Abstract

25.1 Introduction

25.2 Solar cell mathematical modeling

25.3 Yellow Saddle Goatfish Algorithm-based solar cell extraction

25.4 Results and discussion

25.5 Experimental data measurement of 250 Wp PV module (SVL0250P) using SOLAR-4000 analyzer

25.6 Conclusion

References

Chapter 26. Reactive capability limits for wind turbine based on SCIG for optimal integration into the grid

Abstract

26.1 Introduction

26.2 Literature survey and grid code requirements

26.3 Reactive capability limits for squirrel cage induction generator

26.4 Estimation of reactive power limits for the grid side system

26.5 Reactive capability for DC bus capacitor

26.6 Validation results

26.7 Conclusion

Abbreviations

Appendix A

References

Chapter 27. Demand-side strategy management using PSO and BSA for optimal day-ahead load shifting in smart grid

Abstract

27.1 Introduction

27.2 DSM driven approaches

27.3 Mathematical formulation of the problem

27.4 Proposed demand management optimization algorithm

27.5 Energy management of the proposed system

27.6 Results and discussion

27.7 Conclusion

References

Chapter 28. Optimal power generation and power flow control using artificial intelligence techniques

Abstract

28.1 Introduction

28.2 Conventional methods

28.3 Artificial neural network and fuzzy logic to optimal power flow

28.4 Genetic algorithm

28.5 Application of expert system to power system

28.6 Assessment of optimal power flow by game playing concept

References

Chapter 29. Nature-inspired computational intelligence for optimal sizing of hybrid renewable energy system

Abstract

29.1 Introduction

29.2 Mathematical hybrid system model

29.3 Optimization formulation

29.4 Nature-inspired algorithms

29.5 Advantages and limitations of the algorithms

29.6 Numerical data

29.7 Results and discussion

29.8 Findings of the study

29.9 Conclusion and future directions

Acknowledgments

References

Chapter 30. Optimal design and techno-socio-economic analysis of hybrid renewable system for gird-connected system

Abstract

30.1 Introduction

30.2 Motivation and potential benefits of hybrid renewable sources

30.3 Hybrid renewable energy system design and optimization

30.4 Availability of renewable sources and utilization for case study

30.5 Modeling of hybrid renewable system components

30.6 Explanation of problem and methodology for case study

30.7 Results and discussion

30.8 Conclusion

Acknowledgment

References

Chapter 31. Stand-alone hybrid system of solar photovoltaics/wind energy resources: an eco-friendly sustainable approach

Abstract

31.1 Introduction

31.2 Renewable energy sources

31.3 Hybrid renewable energy systems

31.4 Modeling of SPV/wind HRES

31.5 Optimization and sizing of SPV/wind HRES

31.6 Future of SPV/wind HRES

31.7 Conclusion

References

Index

Copyright

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List of contributors

Parimal Acharjee,     Department of Electrical Engineering, National Institute of Technology (NIT), Durgapur, India

Kammogne Soup Tewa Alain,     Laboratory of Condensed Matter, Electronics and Signal Processing (LAMACETS), Department of Physic, Faculty of Sciences, University of Dschang, Dschang, Cameroon

Najib M. Alfakih,     State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing, P.R. China

Mudassar Ali,     Department of Telecommunication and Information Engineering, University of Engineering and Technology, Taxila, Taxila, Pakistan

Mohamed M. Aly,     Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt

Karima Amara,     Electrical Engineering Advanced Technology Laboratory (LATAGE), Mouloud Mammeri University, Tizi Ouzou, Algeria

P. Anandhraj,     Research Scholar, Anna University

E. Artigao-Andicoberry,     Renewable Energy Research Institute and DIEEAC-ETSII-AB, Universidad de Castilla-La Mancha, Albacete, Spain

P. Arvind,     Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India

Ahmad Taher Azar

Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

Meryeme Azaroual,     Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco

Lhoussaine Bahatti,     IESI Laboratory, ENSET Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco

Naglaa K. Bahgaat,     Electrical Communication Department, Faculty of Engineering, Canadian International College (CIC), Giza, Egypt

Rachid Bannari,     Laboratory Systems Engineering, Ensa, Ibn Tofail University Kenitra, Kenitra, Morocco

Agnimitra Biswas,     Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, India

Aashish Kumar Bohre,     Department of Electrical Engineering, National Institute of Technology (NIT), Durgapur, India

David Borge-Diez,     Department of Electrical and Systems Engineering and Automation, University of León, Campus de Vegazana, S/N, León, Spain

Sourav Chakraborty,     Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India

Chakib Chatri,     Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco

Mohamed Cherkaoui,     Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco

Elmostafa Chetouani,     Exploitation and Processing of Renewable Energy Team, Laboratory of Electronics, Instrumentation and Energy, Department of Physics, Faculty of Sciences, University of Chouaib Doukkali, El Jadida, Morocco

Antonio Colmenar-Santos,     Department of Electric, Electronic and Control Engineering, UNED, Juan Del Rosal, 12 – Ciudad Universitaria, Madrid, Spain

M.L. Corradini,     School of Science and Technology, Mathematics Division, University of Camerino, Italy

Jesús De-León-Morales,     Department of Electrical Engineering, Faculty of Mechanical and Electrical Engineering, Autonomous University of Nuevo León, San Nicolás de los Garza, Mexico

Hakim Denoun,     Electrical Engineering Advanced Technology Laboratory (LATAGE), Mouloud Mammeri University, Tizi Ouzou, Algeria

M. Edwin,     Department of Mechanical Engineering, University College of Engineering, Nagercoil, Anna University Constituent College, Nagercoil, India

Abderrahim El Fadili,     FST Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco

Ismail El Kafazi,     Laboratory SMARTILAB, Moroccan School of Engineering Sciences, EMSI Rabat, Rabat, Morocco

Abdelmounime El Magri,     IESI Laboratory, ENSET Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco

Kamal Elyaalaoui,     Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco

Youssef Errami,     Laboratory of Electronics, Instrumentation and Energy, Team of Exploitation and Processing of Renewable Energy, Department of Physics, Faculty of Science, University of Chouaib Doukkali, El Jadida, Morocco

Arezki Fekik

Akli Mohand Oulhadj University, Bouira, Algeria

Electrical Engineering Advanced Technology Laboratory (LATAGE), Mouloud Mammeri University, Tizi Ouzou, Algeria

Marco A. Flores,     Instituto de Investigacion en Energia IIE, Universidad Nacional Autónoma de Honduras (UNAH), Tegucigalpa, Honduras

Aldo José Flores-Guerrero,     Department of Electrical Engineering, Faculty of Mechanical and Electrical Engineering, Autonomous University of Nuevo León, San Nicolás de los Garza, Mexico

Diriba Kajela Geleta

Department of Mathematics, Punjabi University, Patiala, India

Department of Mathematics, Madda Walabu University, Bale Robe, Ethiopia

Sunitha George,     Department of Instrumentation and Control Engineering, Netaji Subhas University of Technology, Dwarka, India

Fouad Giri,     LAC Laboratory, University of Caen Normandie, Caen, France

Anubhav Goel,     Department of Polymer and Process Engineering, Indian Institute of Technology Roorkee, Roorkee, India

E. Gómez-Lázaro,     Renewable Energy Research Institute and DIEEAC-ETSII-AB, Universidad de Castilla-La Mancha, Albacete, Spain

Tulasichandra Sekhar Gorripotu,     Department of Electrical & Electronics Engineering, Sri Sivani College of Engineering, Srikakulam, India

Rajat Gupta,     Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, India

Susana V. Gutiérrez-Martínez,     Department of Electrical Engineering, Faculty of Mechanical and Electrical Engineering, Autonomous University of Nuevo León, San Nicolás de los Garza, Mexico

Mohamed Lamine Hamida,     Electrical Engineering Advanced Technology Laboratory (LATAGE), Mouloud Mammeri University, Tizi Ouzou, Algeria

I. Hammou Ou Ali,     Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco

A. Honrubia-Escribano,     Renewable Energy Research Institute and DIEEAC-ETSII-AB, Universidad de Castilla-La Mancha, Albacete, Spain

Amjad J. Humaidi,     Department of Control and Systems Engineering, University of Technology, Baghdad, Iraq

Ibraheem Kasim Ibraheem,     Department of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq

Rajaa Naji E.L. Idrissi,     Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco

G. Ippoliti,     Department of Information Engineering, Marche Polytechnic University, Ancona, Italy

Jagadish,     Department of Mechanical Engineering, National Institute of Technology Raipur, Raipur, India

D.B. Jani,     Gujarat Technological University—GTU, Government Engineering College, Dahod, India

Francisco Jurado,     Department of Electrical Engineering, University of Jaén, Jaén, Spain

Nashwa Ahmad Kamal

Faculty of Engineering, Cairo University, Giza, Egypt

International Group of Control Systems (IGCS), Riyadh, Saudi Arabia

Salah Kamel

Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt

Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt

Faizan Arif Khan,     Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India

Cheshta Jain Khare,     Shri G.S. Institute of Technology and Science, Indore, India

Vikas Khare,     School of Technology Management & Engineering (STME), NMIMS University, Indore, India

Mohammed Kissaoui,     IESI Laboratory, ENSET Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco

Deepak Kumar,     Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India

Vineet Kumar,     Division of Instrumentation and Control Engineering, Netaji Subhas University of Technology, Dwarka, India

Rachid Lajouad,     IESI Laboratory, ENSET Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco

Ana-Rosa Linares-Mena,     Department of Electric, Electronic and Control Engineering, UNED, Juan Del Rosal, 12 – Ciudad Universitaria, Madrid, Spain

Mohamed Maaroufi,     Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco

Tahir Nadeem Malik,     Department of Electrical Engineering, University of Engineering and Technology, Taxila, Taxila, Pakistan

Gaurav Manik,     Department of Polymer and Process Engineering, Indian Institute of Technology Roorkee, Roorkee, India

Mukhdeep Singh Manshahia,     Department of Mathematics, Punjabi University, Patiala, India

S. Martín-Martínez,     Renewable Energy Research Institute and DIEEAC-ETSII-AB, Universidad de Castilla-La Mancha, Albacete, Spain

Ahmed S. Menesy

Department of Electrical Engineering, Faculty of Engineering, Minia University, Minya, Egypt

State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing, P.R. China

Shikha Mittal,     Department of Mathematics, Jesus and Mary College, University of Delhi, New Delhi, India

Amal Amin Mohamed,     Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt

K. Mohana Sundaram

EEE Department, KPR Institute of Engineerimg and Technology, Coimbatore

Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

College of Computer and Information Sciences, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia

Enrique-Luis Molina-Ibáñez,     Department of Electric, Electronic and Control Engineering, UNED, Juan Del Rosal, 12 – Ciudad Universitaria, Madrid, Spain

Muhammad Naeem,     Department of Electrical Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan

M. Saranya Nair,     School of Electronics Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, India

Abdellatif Obbadi,     Laboratory of Electronics, Instrumentation and Energy, Team of Exploitation and Processing of Renewable Energy, Department of Physics, Faculty of Science, University of Chouaib Doukkali, El Jadida, Morocco

G. Orlando,     Department of Information Engineering, Marche Polytechnic University, Ancona, Italy

M. Ouassaid,     Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco

Mohammed Ouassaid,     Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco

Nitai Pal,     Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India

P. Pandiyan,     Associate Professor, EEE Department, KPR Institute of Engineering and Technology, Coimbatore

Ramana Pilla,     Department of Electrical & Electronics Engineering, GMR Institute of Technology, Rajam, Srikakulam, India

Farhan Qamar,     Department of Telecommunication and Information Engineering, University of Engineering and Technology, Taxila, Taxila, Pakistan

Nouman Qamar,     Department of Electrical Engineering, University of Engineering and Technology, Taxila, Taxila, Pakistan

K.P.S. Rana,     Division of Instrumentation and Control Engineering, Netaji Subhas University of Technology, Dwarka, India

Kengne Romanic,     Laboratory of Condensed Matter, Electronics and Signal Processing (LAMACETS), Department of Physic, Faculty of Sciences, University of Dschang, Dschang, Cameroon

Enrique Rosales-Asensio,     Department of Electrical Engineering, University of Las Palmas de Gran Canaria, Campus de Tafira S/N, Las Palmas de Gran Canaria, Spain

Francisco Ruiz,     Instituto de Investigacion en Energia IIE, Universidad Nacional Autónoma de Honduras (UNAH), Tegucigalpa, Honduras

Syed Hasan Saeed,     Department of Electronics & Communication Engineering, Integral University, Lucknow, India

Smail Sahnoun,     Laboratory of Electronics, Instrumentation and Energy, Team of Exploitation and Processing of Renewable Energy, Department of Physics, Faculty of Science, University of Chouaib Doukkali, El Jadida, Morocco

Yashwant Sawle,     School of Electrical Engineering, Vellore Institute of Technology (VIT), Vellore, India

Nitish Sehgal,     Department of Instrumentation and Control Engineering, Netaji Subhas University of Technology, Dwarka, India

S. Joseph Sekhar,     Department of Engineering, Shinas College of Technology, University of Technology and Applied Sciences, Shinas, Sultanate of Oman

Ali Selim

Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt

Department of Electrical Engineering, University of Jaén, Jaén, Spain

Fernando E. Serrano

Instituto de Investigacion en Energia IIE, Universidad Nacional Autónoma de Honduras (UNAH), Tegucigalpa, Honduras

Universidad Tecnolgica Centroamericana (UNITEC), Zona Jacaleapa, Tegucigalpa, Honduras

Hamdy M. Sultan,     Department of Electrical Engineering, Faculty of Engineering, Minia University, Minya, Egypt

Mahrous A. Taher,     Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt

Ahmad Taher Azar

Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

College of Computer and Information Sciences, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia

P.R. Thakura,     Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India

Sundarapandian Vaidyanathan,     Research and Development Centre, Vel Tech University, Chennai, India

H.K. Verma,     Shri G.S. Institute of Technology and Science, Indore, India

R. Villena-Ruiz,     Renewable Energy Research Institute and DIEEAC-ETSII-AB, Universidad de Castilla-La Mancha, Albacete, Spain

Boaz Wadawa,     Laboratory of Electronics, Instrumentation and Energy, Team of Exploitation and Processing of Renewable Energy, Department of Physics, Faculty of Science, University of Chouaib Doukkali, EI Jadida, Morocco

Aziz Watil,     IESI Laboratory, ENSET Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco

Nacera Yassa,     Akli Mohand Oulhadj University, Bouira, Algeria

Preface

Ahmad Taher Azar¹, ² and Nashwa Ahmad Kamal³, ⁴, ¹Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt, ²College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia, ³Faculty of Engineering, Cairo University, Giza, Egypt, ⁴International Group of Control Systems (IGCS), Cairo, Egypt

Renewable energy systems play a decisive role on the path toward meeting the energy demands of a growing global population and the economic needs of countries at all stages of development if these goals are to be achieved in a climate-friendly environment. Indeed, given contemporary climate change challenges, renewable energy has a dual role to play in both mitigation and adaptation efforts. The search for sustainable energy, therefore, continues to dominate the 21st-century research, business, and policy. In addition, renewable energy systems exhibit changing dynamics, nonlinearities, and uncertainties and challenges that require advanced control and optimization strategies to solve effectively. The use of more efficient control and optimization strategies would not only enhance the performance of these systems but would also reduce the cost per kilowatt-hour produced. This book aims to cross-pollinate the recent advances in the study of renewable energy control systems by bringing together diverse scientific breakthroughs on the modeling, control, and optimization of renewable energy systems by leading energy systems engineering researchers in this field.

About the book

This book "Renewable Energy Systems: Modeling, Optimization, and Control," consisting of 31 contributed chapters, is written by subject experts who have specialization in various topics addressed in this book. The special chapters have been brought out in this book after a rigorous review process in the broad areas of modeling, optimization, and control. Special importance was given to chapters offering practical solutions and novel methods for the recent research problems in the mathematical modeling and applications of renewable energy systems.

Objectives of the book

This book has a special focus on renewable energy control systems. This book will have chapters with a current focus on research and novel solutions to so many problems in that field. This book will cover most of the modeling, control theorems, and optimization techniques, altogether which will solve many scientific issues for researchers in the field of renewable energy and can be considered a good reference for young researchers. Many multidisciplinary applications have been discussed in this book with their new fundamentals, modeling, control, and experimental results. Simply, this book will achieve the gap between different interdisciplinary applications starting from mathematical concepts, modeling, optimization, and up to recent control techniques.

Organization of the book

This well-structured book consists of 31 full chapters.

Book features

• Deals with the recent research problems in the area of renewable energy systems

• Presents various techniques of modeling, optimization, and advanced control techniques for renewable energy

• Contains a good literature survey with a long list of references

• Well written with a good exposition of the research problem, methodology, block diagrams, and mathematical techniques

• Lucidly illustrates with numerical examples and simulations

• Discusses details of engineering applications and future research areas

Audience

This book is primarily meant for researchers from academia and industry, who are working on renewable energy research areas, electrical engineering, control engineering, electronic engineering, mechanical engineering, and computer science. The book can also be used at the graduate or advanced undergraduate level as a textbook or major reference for courses such as power systems, control systems, electrical devices, scientific modeling, and computational science.

Acknowledgments

As the editors, we hope that the chapters in this well-structured book will stimulate further research in renewable energy systems and utilize them in real-world applications.

We hope sincerely that this book, covering many different topics, will be very useful for all readers.

We would like to thank all the reviewers for their diligence in reviewing the chapters.

Special thanks goes to Elsevier, especially the book Editorial team.

Chapter 1

Efficiency maximization of wind turbines using data-driven Model-Free Adaptive Control

M.L. Corradini¹, G. Ippoliti² and G. Orlando²,    ¹School of Science and Technology, Mathematics Division, University of Camerino, Italy,    ²Department of Information Engineering, Marche Polytechnic University, Ancona, Italy

Abstract

In this chapter, the problem of efficiency maximization of a wind turbine (WT) operating in the region of medium wind speed has been addressed using a data-driven control algorithm based on the Model-Free Adaptive Control. An equivalent dynamic linearization model is used, obtained adopting a dynamic linearization technique based on pseudopartial derivatives. The proposed algorithm is inspired by the very recent paper by Liu–Yang, where a data-driven adaptive sliding mode controller has been proposed to account also for prescribed performance constraints. A rigorous stability analysis is presented here, achieved modifying the forms of the sliding surface and of the control law but still retaining the main setup presented in the source paper. Validation of these techniques has been performed using the standard 5-MW WT by NREL, operating in the region of medium wind speed, using the recognized high-fidelity simulation tool FAST by NREL. Comparison data are reported.

Keywords

Data-driven control; Model-Free Adaptive Control; wind turbines; efficiency maximization

1.1 Introduction

Modeling and control of wind turbines (WTs) is a challenging task (Johnson, Pao, Balas, & Fingersh, 2006), both because their aerodynamics are highly complex and nonlinear and because rotors are forced by stochastic and turbulent wind inflow fields. In general, reducing the cost of wind energy is the key factor driving a successful growth of the wind energy sector, and manufacturers increasingly turn to more refined control systems (Bossanyi et al., 2012; Ozbek & Rixen, 2013) requiring detailed models of the turbine to capture the complete dynamic behavior of the system (Simani & Castaldi, 2013; van der Veen, van Wingerden, Fleming, Scholbrock, & Verhaegen, 2013) to improve efficiency. System identification techniques may allow improvements in controller design through the analysis and understanding of the system dynamics (van der Veen et al., 2013): several contributions have appeared on the identification of linear, time-invariant models of WTs (Hansen, Thomsen, Fuglsang, & Knudsen, 2006; Iribas & Landau, 2009; Iribas-Latour & Landau, 2013; van Baars & Bongers, 1994), but it is apparent that nonlinearity must be considered in control design (Bossanyi, 2000) since WTs are nonlinear systems operating under large wind speed variations. Identification techniques for nonlinear systems and for linear parameter-varying systems are an active research area, though these methods still present significant challenges in terms of reliability and computational complexity, thus making their application under closed-loop conditions troublesome (van Wingerden & Verhaegen, 2009).

In the framework of the discussed identification-based control approaches, a dynamic linearization technique using pseudo-partial derivatives (PPDs) has been recently proposed and coupled with a data-driven control method based on Model-Free Adaptive Control (MFAC). The MFAC algorithm has been recently proposed for a class of general discrete-time nonlinear systems. First discussed in Hou (1994), extended in Hou and Jin (2011), and finally thoroughly formalized in Hou and Jin (2014), MFAC makes use of an equivalent dynamic linearization model obtained adopting a dynamic linearization technique based on PPDs. In addition to a number of interesting features, discussed in Hou and Jin (2011) and ranging from low cost and easy applicability, successful implementation in practical applications, and absence of training phases, it is worth to notice that BIBO stability and closed-loop convergence of the tracking error have been theoretically proved under mild assumptions (Hou & Jin, 2011). In this framework, the proposal has been very recently presented (Liu & Yang, 2019) to couple the so-called prescribed performance control (PPC) (Bechlioulis & Rovinthakis, 2008, 2009) with MFAC, to embed a constraint on the tracking error within the MFAC mechanism. A complement to this interesting approach has been proposed in the considered algorithm. In particular, still retaining the main setup presented in Liu and Yang (2019), the forms of the sliding surface and of the control law have been modified to provide a rigorous proof ensuring the boundedness of the control law. Furthermore, a rigorous stability of the closed-loop system is provided, leading to the definition of suitable constraints on the gain of the sliding-mode-based control term.

To support the theoretical development with significant test data, the proposed approach has been applied to the problem of efficiency maximization of a 5-MW WT operating in region 2 (medium-speed region) using the high-fidelity widely recognized simulation tool FAST (NREL-NWTC, n.d.) (data provided by FAST are comparable to experimental data in studies about WTs). A comparative analysis has been performed, to quantify the improvement in terms of control accuracy of the proposed algorithm with respect to both the approaches described in Hou and Jin (2011) and Liu and Yang (2019).

The chapter is organized as follows. Section 1.2 presents some preliminary issues about the wind control problem in region 2 and the considered control model. The main technical results for the proposed control solution have been reported in Section 1.3. A study using FAST addressing the control of a 5-MW WT operating in the medium-speed region is reported in Section 1.4. The chapter ends with a few comments reported in Section 1.5.

1.2 Problem statement

1.2.1 The problem of optimal power extraction for wind turbines

As widely known, variable-speed WTs operate differently depending on wind speed (Johnson et al., 2006). It is customary to consider three different regions of operation, and the so-called region 2 (or medium wind speed region associated to wind speeds of medium entity) is an operational mode where generator torque control is used with the objective of maximizing wind energy capture. The aerodynamic power extracted by the WT from the wind is given by Bianchi, Battista, and Mantz (2007):

(1.1)

where is the WT rotor radius, is the air density, and is the wind speed. The torque extracted by the turbine from the wind is given by:

(1.2)

The power coefficient expressed the turbine efficiency in converting the wind energy into mechanical energy (Bianchi et al., 2007), is a nonlinear function (Monroy & Alvarez-Icaza, 2006; Siegfried, 1998), and depends on blade aerodynamic design and WT operating conditions. It depends nonlinearly on the blade pitch angle (equal to zero since pitch control is not active in region 2) and the so-called tip speed ratio , defined as follows (Qiao, Qu, & Harley, 2009):

(1.3)

being the turbine angular shaft speed.

This chapter addresses generator torque control of variable-speed WTs aimed at maximizing the energy capture when operating in region 2 . The control objective is to achieve and maintain the maximum power coefficient regardless of wind speed. In other words, the turbine has to be driven at the unique WT shaft rotational speed ensuring the maximum power coefficient (Bianchi et al., 2007), in view of the definition of in Eq. (1.3). According to Eq. (1.1), indeed, when is controlled at the maximum value, the maximum mechanical power is extracted from the wind energy. For the NREL WT (Jonkman, Butterfield, Musial, & Scott, 2009), the maximum power coefficient is achieved for a tip speed ratio value of . Thus the optimal WT angular shaft speed is given by:

(1.4)

The mechanical equation governing the turbine can be given as follows (Johnson et al., 2006):

(1.5)

This equation models the drivetrain dynamics, where is the rotor speed, is the aerodynamic torque and is the electrical generator torque. Moreover, is the high-speed shaft (HSS)-to-low-speed shaft gearbox ratio, and is the moments of inertia about the rotation axis.

1.2.2 Data-driven Model-Free Adaptive Control

Consider the following discrete-time SISO nonlinear system (Hou & Jin, 2011):

(1.6)

where and are the system input and output at time , and are unknown orders, and is an unknown nonlinear function. The partial form dynamic linearization (PFDL) of the plant (Eq. 1.6) is based on the following assumptions.

Assumption 2.1

The partial derivatives of with respect to the control input , are continuous, being a positive discrete constant known as control input length constant of linearization for the discrete-time nonlinear system.

Define: , , , , with for .

Assumption 2.2

The plant is generalized Lipschitz, that is, , , and

The PPD-based model of the plant (Eq. 1.6) relies on the following theorem (Hou & Jin, 2011).

Theorem 2.1

For the nonlinear system [Eq. (1.6)] satisfying Assumptions 2.1 and 2.2, there exists a parameter vector , called the PPD vector, such that the plant can be transformed into the following equivalent PFDL description

(1.7)

where and , where is a positive constant. Following Hou and Jin (2011), the estimate of the unknown PPD vector can be derived using the modified projection algorithm starting from the following cost function:

(1.8)

obtaining:

(1.9)

(1.10)

with and is a positive design constant.

Remark 2.1

Due to Eq. (1.10), it can be assumed, without loss of generality, that .

The control problem addressed in this chapter is the tracking of a constant reference output variable . As usual, the quantity is the tracking error, whose dynamics can be easily derived from Eq. (1.7):

(1.11)

with

(1.12)

1.3 Control design

Before proceeding to the design of the control input, the following assumption (Hou & Jin, 2011) is required to prove the stability and convergence of the overall control scheme.

Assumption 3.1

The first element of the PPD vector satisfies , where is a small positive constant.

Following Liu and Yang (2019), a PPC requirement is considered. In particular, a positive, decreasing, discrete-time sequence is defined as follows:

(1.13)

with . It is required that

(1.14)

where is the tracking error. According to Liu and Yang (2019), the following transformed error is introduced:

(1.15)

thus transforming the initial problem containing the constraint on the tracking error into an unconstrained problem. Using Eq. (1.15), the following sliding surface is defined:

(1.16)

differently from Liu–Yang.

Remark 3.1

In this chapter, a sliding mode based control law will be provided solving the PPC problem. The reason for proposing the sliding surface [Eq. (1.16)] is that a rigorous stability analysis will be provided guaranteeing both the achievement of the tracking performances and the boundedness of the closed-loop variables. Such stability analysis will be performed, differently from (Liu and Yang, 2019) studying the behavior of the true plant [Eq. (1.17)] (not the estimated one ) fed by the proposed sliding mode control law.

Furthermore, it should be recalled that, since and are bounded in view of Theorem 2.1, there exists such that for it holds (Hou & Jin, 2011)

(1.17)

where and are positive constants, and there exists such that

(1.18)

Consider the following control input

(1.19)

where

(1.20)

with to be defined in the following, and

(1.21)

where , and with properly chosen positive constants. From the definition of the tracking error, one has:

(1.22)

Defining , it follows:

(1.23)

and after some manipulations one gets:

(1.24)

where

(1.25)

(1.26)

(1.27)

Lemma 3.1

Consider the plant [Eq. (1.7)] satisfying Assumption 3.1, controlled by the control input [Eq. (1.19)]. Then, there exists such that .

The proof follows the lines of Hou and Jin (2011) and is omitted for brevity.

Defining:

(1.28)

in view of Remark 2.1, Assumption 3.1 and Eq. (1.17), it holds:

(1.29)

and introducing the following definitions:

(1.30)

(1.31)

(1.32)

due to Eq. (1.29), , , and are bounded as follows:

(1.33)

(1.34)

(1.35)

Moreover, comparing Eqs. (1.25), (1.26), and (1.30), one has:

(1.36)

(1.37)

As a consequence, can be written as:

(1.38)

Theorem 3.1

Consider the plant [Eq. (1.7)] satisfying Assumption 3.1, controlled by the control input [Eq. (1.19)]. Under the condition:

(1.39)

for in Eq. (1.7) with suitable values of , the gain can be properly designed such that the sliding variable is bounded. As a consequence, is bounded and the condition [Eq. (1.14)] is satisfied.

Proof

The proof consists of two steps. First, it will be proved that , and next, it will be shown that a region exists bounding the sliding variable.

Step 1, .

Define:

The condition requires that

(1.40)

Moreover, due to Eq. (1.40), it follows:

(1.41)

Taking the worst case, and defining , conditions (1.40) correspond to:

(1.42)

and the following strongest condition will be considered instead:

(1.43)

Recalling that, according to Eq. (1.17), , condition (1.43) is fulfilled if:

(1.44)

which requires:

(1.45)

or, as a stronger condition:

(1.46)

Step 1, .

Define:

(1.47)

The condition requires that:

(1.48)

Considering the worst case and definition (1.47), inequalities (1.48) correspond to:

(1.49)

The following strongest condition will be considered instead:

(1.50)

with .

Taking into account Lemma 3.1 and recalling that, according to Eq. (1.17), , from Eq. (1.50), the following conditions on can be derived:

(1.51)

which requires:

(1.52)

or, as a stronger condition:

(1.53)

To have a feasible solution interval for , recalling Eq. (1.17), and considering also Eq. (1.46), one has the final inequalities system on :

(1.54)

that is,

(1.55)

which corresponds to Eq. (1.39).

Step 2. Consider expression (1.38). Due to conditions derived in Step 1 when , it holds , that is,

(1.56)

Moreover, using Eq. (1.43) and taking the worst case, one has:

(1.57)

(1.58)

Taking into account Eqs. (1.56)– (1.58), it follows:

(1.59)

Consider again expression (1.38). Due to conditions derived in Step 1 when , it holds , that is,

(1.60)

Using Eq. (1.50) and taking the worst case, one has:

(1.61)

(1.62)

Taking into account Eqs. (1.60)– (1.62), it follows:

(1.63)

Collecting the results obtained in both cases and one gets:

(1.64)

where and . Define

(1.65)

It is straightforward to see that, once this region is entered by the sliding variable at the time instant , then . In fact, if and , it means that. Since , one has . Analogously, if and , it means that . Since , one has .

1.4 Simulation study using FAST

The application of a rotor speed control approach would require the availability of a reference speed ensuring the tracking of the maximum delivered power point, but this is hindered by the strongly nonlinear nature of Eq. (1.2). The approach taken here is to consider a PFDL description of the overall nonlinear dynamics connecting the aerodynamic torque (output variable) and the electrical torque (input variable) under the effect of the (not manipulable) external input (wind speed), which basically affects the aerodynamic torque through the nonlinear power coefficient . In such a framework, the WT rotor speed acts as an internal (state) variable governing the system dynamics. The aerodynamic torque can be estimated by the torque transmitted through the HSS, which can be measured from a strain gage mounted on the HSS as reported by NREL in NREL-NWTC (2015).

With the described setting, a nonlinear model of the form [Eq. (1.6)] has been considered and a PPD-based model has been derived. The addressed control problem can be now stated as an output tracking problem with respect to the optimal torque in the region 2 control law, built based on instantaneous rotor speed measurements as proposed in Jonkman et al. (2009).

The proposed controller has been tested by intensive simulations using the NREL FAST code. The FAST simulator is a high fidelity aeroelastic simulator of two- and three-bladed horizontal-axis WTs (Jonkman & Buhl, 2005; NREL-NWTC, n.d.) widely adopted for WT design and certification (Buhl & Manjock, 2006; Manjock, 2005). Simulations have been performed using the NREL 5-MW WT, whose parameters have been derived from (Jonkman et al., 2009). The FAST wind data (NREL-NWTC, n.d.) used for validation tests are shown in Fig. 1.1 (mean value 8 m s−1). Initial conditions have been set as .

Figure 1.1 Wind inflow.

The following parameters have been used for simulation tests: , , , , , , , , .

Some of the performed tests have been reported in Figs. 1.2–1.4. Fig. 1.2 shows the angular rotor speed, Fig. 1.3 shows the optimal generator-torque (Jonkman et al., 2009) tracking error, and Fig. 1.4 shows the tip speed ratio. A comparison of the proposed control approach with respect to the performances obtained with the control algorithms described in Hou and Jin (2011) and Liu and Yang (2019) has been reported.

Figure 1.2 Rotor speed. (A) Proposed approach; (B) approach proposed in Liu and Yang (2019); and (C) approach proposed in Hou and Jin (2011).

Figure 1.4 Tip speed ratio. (A) Proposed approach; (B) approach proposed in Liu and Yang (2019); and (C) approach proposed in Hou and Jin (2011).

Figure 1.3 Tracking error. (A) Proposed approach; (B) approach proposed in Liu and Yang (2019); and (C) approach proposed in Hou and Jin (2011).

To analyze the performance of the proposed controller in terms of fulfillment of the prescribed performance requirement, the IAE criterion (i.e., the integral of the absolute value of the tracking error) has been computed for the considered controller starting from the time instant to eliminate the transient influence. The results are reported in Table 1.1. It can be noticed that an improvement has been obtained with respect to the control algorithm described in Hou and Jin (2011) of approximately 8% and a comparable performance has been achieved with respect to Liu and Yang (2019). The reported results show that the proposed controller slightly outperforms the solution in Liu and Yang (2019), but it is worth noting that the algorithm in Liu and Yang (2019) has been tuned at its best, by trial and errors, setting parameters without any methodological support.

Table 1.1

1.5 Conclusions

The present study is inspired by very recent papers (Hou & Jin, 2011; Liu & Yang, 2019), where data-driven MFAC controllers have been presented. Due to the presence of an unsatisfactory theoretical proof of convergence in the original paper (Liu & Yang, 2019), a rigorous stability analysis is here proposed with the considered algorithm, achieved modifying the forms of the sliding surface and of the control law but still retaining the main setup presented in the source paper (Liu & Yang, 2019). The presented careful comparative analysis, performed addressing the control of the three-blade NREL 5-MW WT (Jonkman et al., 2009) aiming at efficiency maximization, has been shown to provide very satisfactory results, showing some performance improvement, in terms of tracking accuracy, of the proposed control law with respect to the available literature. A number of future developments of the present chapter can be foreseen, namely the extension to multioutput systems, the inclusion of an explicit robustness feature with respect to external disturbances affecting the plant, and the extension to pitch control in the case of a turbine operating in the high wind speed region.

References

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Chapter 2

Advanced control design based on sliding modes technique for power extraction maximization in variable speed wind turbine*

Aldo José Flores-Guerrero, Jesús De-León-Morales and Susana V. Gutiérrez-Martínez,    Department of Electrical Engineering, Faculty of Mechanical and Electrical Engineering, Autonomous University of Nuevo León, San Nicolás de los Garza, Mexico

Abstract

This chapter deals with the control problem of the power extraction maximization in a variable speed wind turbine based on doubly fed induction generator. Then, a solution to this problem is to keep the turbine operating at the maximum aerodynamic efficiency, leading to the rotor rotational speed to its optimum value by means of regulating rotor voltages in the generator.

To achieve this objective, a super twisting controller is designed to track the optimal rotor speed value and compensate bounded matching perturbations. Furthermore, a stability analysis is presented based on the Lyapunov approach, where sufficient conditions are given to ensure the finite time convergence.

The simulation results are given to illustrate the performance of the proposed control strategy on a three-bladed 1.5-MW variable speed wind turbine using Fatigue, Aerodynamic, Structures and Turbulence and TurbSim simulator from National Renewable Energy Laboratory interfaced with MATLAB–Simulink.

Keywords

Wind turbine; power control; nonlinear control; sliding mode; MPPT; DFIG

2.1 Introduction

Through the years the society and industry have experienced and exploited different sources of energy; however, electrical energy has become one of the most important, due to its socio-economical benefits. Nevertheless, the real demand for electrical energy is increasing due to technological development and increased productivity in industry and energy consumption in homes. Thus large-scale conversion systems are required. Therefore the costs of power generation increase, which leads to the implementation of different power electric plants for satisfying the electric power demand, such as thermal, nuclear, geothermal, and combined cycle.

Among the different power plants, thermal plants have been widely used over the world in the last decades, thanks to the low generation cost, high conversion efficiency, easy installation, and less construction space required.

Regardless, through the years this process has presented several environmental risks because of coal using. The burning of coal and other fossil fuels release pollutants to the atmosphere, such as carbon dioxide and sulfur dioxide, which are harmful and are the cause of greenhouse effect, which leads to global warming, spread of the diseases, and the melting of ice caps.

These environmental risks and the possible energy crisis have led to improve and implement new technologies for electric power generation based on clean energies solutions, such as solar and wind energy, which are the most popular. Among these energy sources, wind power has experienced an important growth in the last decades, due to its socio-economical profits and environmental friendly energy production because no pollution is involved in this process.

According to the information published by the World Wide Energy Association in 2020, the overall capacity of all wind turbines (WTs) installed in the end of 2019 reached 650.8 GW, which can cover more than 6% of the global electricity demand. This growth implies new challenges in engineering and science.

On the other hand, thanks to the advances in power electronics and aerodynamic engineering, the WTs become more attractive for power generation; thus advanced control strategies for power generation are required. These controls should be capable to reduce the cost of wind energy production by increasing turbine efficiency and the life time of components and structures.

2.1.1 A description of wind turbines

The circulation of the air masses in the atmosphere (well known as wind) is a consequence of the temperature and pressure gradients, due to the interaction of the energy that comes from the sun with the air masses, generating differences in density between them.

Thanks to the application of aerodynamic principles and mechanical links together with electrical devices, it is possible to obtain electrical energy from the kinetic energy available in the wind. This energy conversion process is carried out by WTs.

The WTs are complex electromechanical devices comprised of many components with different functions, interacting with a changing environment. The main components of a WT, shown in Fig. 2.1, are (1) the rotor, which includes the blades, hub, and aerodynamic surfaces; (2) the drive train, which is composed by the gearbox (if any), low and high speed shafts referred to the rotor and generator, respectively, the mechanical brakes and couplings connecting them; (3) the main frame, which provides support for the mounting and proper alignment of the drive train components, along with the nacelle that protects them from the environmental conditions (Manwell, McGowan, & Rogers, 2010); and (4) the tower, which supports the nacelle at an appropriate height to reduce the influence of turbulence and maximize the wind energy. On the top of the tower, the yaw system keeps the rotor shaft aligned with the wind.

Figure 2.1 Wind turbine components.

The conversion process is summarized as follows: the blades connected to the rotor by the hub are moved by the wind causing them to spin, producing a rotation in the low speed shaft. This rotation is transmitted to the high speed shaft through the gearbox, which serves to increase the rotational speed.

The high-speed shaft drives the rotational torque to the generator where the conversion process of mechanical energy into electrical energy is carried out. This conversion requires a fixed input speed or power electronic devices to adapt the output energy to the grid requirements (Márquez, Pérez, Marugán, & Papaelias, 2016).

There exists a theoretical limit based on aerodynamic principles that states the amount of wind energy that can be converted into mechanical energy. This limit receives the name of Betz limit, which is less than 59.3% according to the blade element momentum theory. This limit plays a decisive role in the control designing of WTs to produce electrical power.

2.1.2 Wind turbines structures and operation conditions

WTs can be classified according to: (1) output power, (2) electric machine, (3) speed operation, and (4) turbine orientation, as shown in Fig. 2.2.

Figure 2.2 Wind turbine classification.

Regarding output power, the WTs are classified into: (1) small, (2) medium, and (3) large capacity.

The output power depends on the diameter of the turbine and the blade length, which influence the rotor swept area and thus the amount of captured power.

On the other hand, to provide electrical power to small stores, farms, residences, and communities, small size WTs (rated below 100 kW) have been developed. Furthermore, medium size WTs with power capacity (less than 1 MW) are commonly used in the industry applications. Finally, large size WTs are used in large utility grids (Manwell et al., 2010).

The WTs can be constructed using different electrical machines. The most widely used in the industry are generators. The most common types are: (1) induction generators, (2) synchronous generators, and (3) DC generators.

The DC generators are usually used in small WTs. However, nearly all large-scale WTs use either induction or synchronous generators. For WTs in grid-connected applications, induction generators are the most used, due to its smooth connection to the electrical grid and the reduced costs (Susperregui, Jugo, Lizarraga, & Tapia, 2014).

Another classification of the WTs is, in terms of speed operation, such as (1) constant speed and (2) variable speed WTs. The choice of whether the rotor speed is constant or variable may have some impact on the overall control design.

An example of a constant speed WT is based on the induction generator (squirrel cage) connected to the grid as seen in Fig. 2.3. This WT operates with very little variations in rotor speed since it is directly connected to the grid, which is operating at a fixed frequency. However, it is necessary an external reactive power support to compensate power consumed by the induction machine. Furthermore, for regulating power at high wind speeds, stall control and blade pitch control are required. Despite constant speed configuration is very simple and robust, energy capture from the wind is suboptimal and reactive power compensation is required.

Figure 2.3 Constant speed wind turbine configuration.

On the other hand, WTs with variable speed are classified into (Patil & Bhosle, 2013):

• limited variable speed wind turbines (VSWTs);

• VSWTs with partial-scale power converter; and

• VSWTs with full-scale power converter.

In power generation, the VSWTs can be an attractive option. Advances in power converters, modern control theory and the study of aero-elasticity, enabled WT engineers to design modern WTs capable of adjust speed operation. This offers many benefits, including the reduction of mechanical stresses, high efficiency, high power quality, and acoustic noise reduction, and provides a simple pitch control (Muller, Deicke, & De, 2002).

Different possible configurations of generators and power converters for VSWTs are briefly described as follows.

Limited VSWT shown in Fig. 2.4 is a simple extension of a constant speed WT configuration. The inclusion of a wounded induction machine instead of a squirrel cage induction one allows the turbine to control the rotor speed by means of variable resistances, connected in series with the rotor windings. This fact allows the turbine to operate at a maximum aerodynamic efficiency over 10% of wind speeds. However, there exist some heat losses in the rotor resistances and a capacitor bank is required to compensate the reactive power consumed by the electric machine.

Figure 2.4 Limited variable speed wind turbine.

VSWTs with partial-scale power converter, see Fig. 2.5, employ a doubly fed induction generator (DFIG). The stator of the DFIG is connected to the grid directly (Li & Chen, 2008), while the generator rotor is connected to the grid by a back-to-back AC/DC/AC converter.

Figure 2.5 Variable speed wind turbines with partial-scale power converter.

In this configuration, instead of connecting resistances, a power converter is connected in the rotor windings, allowing the system to use the energy that was wasted in the resistances, and then it facilitates the control of the WT rotor speed around 33% of the synchronous speed. In this way, the WT can operate at maximum aerodynamic efficiency for different wind speeds. The main advantage of this configuration is the reduced converter costs due to the total of power passing through the converter, which is about