Distributed Optimal Control of Large-Scale Wind Farm Clusters: Optimal Active and Reactive Power Control, and Fault Ride Through (Wind Energy Engineering)
Language: English
Published by Academic Press, 2025
- Softcover
- New
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In English.
Seller Inventory # ria9780443292347_new
- Title
- Distributed Optimal Control of Large-Scale Wind Farm Clusters: Optimal Active and Reactive Power Control, and Fault Ride Through (Wind Energy Engineering)
- Author
- Wu, Qiuwei; Huang, Sheng; Wei, Juan; Wang, Pengda; Li, Canbing; Terzija, Vladimir
- Publisher
- Academic Press
- Publication year
- 2025
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0443292345
- ISBN 13
- 9780443292347
- Item weight
- 570 grams
Researchers, faculty, scientists, engineers, R&D, and other industry professionals, as well as graduate and postgraduate students studying and working in wind energy will find this comprehensive resource a valuable addition to their work.
- Presents the latest developments in the distributed optimal control of large-scale wind power plant clusters
- Covers both active and reactive power control, as well as techniques for high voltage ride through (HVRT)
- Provides methodologies to follow set-points from system operators in order to maintain expected voltages
- Includes control algorithms and codes for implementing the control schemes
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About the Author
Qiuwei Wu received the PhD degree in Electrical Engineering from Nanyang Technological University, Singapore, in 2009. He is a professor with the School of Electronics, Electrical Engineering, and Computer Science (EEECS), Queen’s University Belfast, the UK. His research interests are distributed optimal operation and control of low carbon power and energy systems, including distributed optimal control of wind power, optimal operation of active distribution networks, and optimal operation and planning of integrated energy systems.
Sheng Huang received the M.S. and Ph.D. degrees in electrical engineering from the College of Electrical and Information Engineering, Hunan University, Changsha, China, in 2012 and 2016, respectively. Since 2023, he has been a Professor with College of Electrical and Information Engineering, Hunan University. His research interests include renewable energy generation, modeling and integration study of wind power, and permanent magnet synchronous motor systems.
Juan Wei is currently a Postdoc with the College of Electrical and Information Engineering, Hunan University, China. She received her B.S. and M.S. degrees in electrical engineering from the North China Electric Power University, Beijing, China, in 2011 and 2014, and obtained her PhD degree in electrical engineering from Hunan University, China, in 2022. Her research interests include wind power modeling and control, decentralized/distributed voltage control and optimal operation of integrated wind power systems, and high-voltage ride-through and post-event recovery control of large-scale wind farms. Dr. Wei is an Editor of Hunan Electric Power.
Pengda Wang is a Research Associate with College of Electrical and Information Engineering, Hunan University, China. He obtained his PhD degree in Electrical Engineering from the Technical University of Denmark, Denmark, in 2022. His research interests lie in coordinated control of wind power and combined AC/DC grid, including distributed wind power modelling and control, voltage control and active/reactive power control of large-scale wind farms and combined AC/DC grid.
Canbing Li is currently a Professor with the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. He received his B.E. and Ph.D. degrees from Tsinghua University, Beijing, China, in 2001 and 2006, respectively, both in electrical engineering. His research interests include power systems, smart grid, renewable energy with an emphasis on large-scale power system dispatch, economic and secure operation of power systems, energy efficiency and energy saving in smart grid, electric demand management of data centers, vehicle-to-grid technologies.
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