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Distributed Optimal Control of Large-Scale Wind Farm Clusters
Optimal Active and Reactive Power Control, and Fault Ride Through
- 1st Edition - April 4, 2025
- Authors: Qiuwei Wu, Sheng Huang, Juan Wei, Pengda Wang, Canbing Li, Vladimir Terzija
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 2 3 4 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 2 3 5 - 4
Distributed Optimal Control of Large-Scale Wind Farm Clusters: Optimal Active and Reactive Power Control, and Fault Ride Through, a new volume in the Elsevier Wind Energy Engineeri… Read more
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Request a sales quoteDistributed Optimal Control of Large-Scale Wind Farm Clusters: Optimal Active and Reactive Power Control, and Fault Ride Through, a new volume in the Elsevier Wind Energy Engineering series, explores the latest advances in distributed optimal control of large-scale wind farm clusters, also describing distributed optimal control techniques for high voltage ride through (HVRT). Both mathematical formulations and algorithm details are provided, along with MATLAB codes to replicate and implement distributed optimal control schemes. This is a valuable resource for anyone interested in the operation, control, and integration of wind power plants, wind farms, and electricity grids, both at research and operational levels.
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.
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
Researchers, students, and faculty across wind energy, renewable energy, electrical engineering, sustainability
Section I - Introduction
1. Introduction to Large-Scale Wind Power Integration
Section II - Optimal Active Power Control of Large-Scale Wind Farm Clusters
2. Bi-Level Decentralized Active Power Control for Large-Scale Wind Farm Clusters
3. Optimal Active Power Control Based on MPC for DFIG-based Wind Farm Equipped with Distributed Energy Storage Systems
4. Hierarchical Active Power Control of DFIG-based Wind Farm with Distributed Energy Storage Systems based on Alternating Direction Method of Multipliers (ADMM)
5 Hierarchical Optimal Control for Synthetic Inertial Response of Wind Farm Based on Alternating Direction Method of Multipliers (ADMM)
Section III - Optimal Active and Reactive Power Control of Large-Scale Wind Farm Clusters
6. Bi-Level Decentralized Active and Reactive Power Control for Large-Scale Wind Farm Cluster
7. Two-Tier Combined Active and Reactive Power Control for VSC–HVDC Connected Large-Scale Wind Farm Cluster based on Alternating Direction Method of Multipliers (ADMM)
8. Distributed Optimal Active and Reactive Power Control for Wind Farms Based on ADMM
9. ADMM-based Distributed Active and Reactive Power Control for Regional AC Grids with Wind Farms
Section IV - Optimal Voltage Control of Large-Scale Wind Farm Clusters
10. Distributed Voltage Control based on ADMM for Large Scale Wind Farm Cluster connected to VSC HVDC
11. Distributed Optimal Voltage Control for VSC-HVDC Connected Large-Scale Wind Farm Cluster Based on Analytical Target Cascading Method
12. Adaptive Droop-based Hierarchical Optimal Voltage Control Scheme for VSC-HVDC Connected Offshore Wind Farm
13. Distributed Optimal Voltage Control Strategy for AC Grid with DC Connection and Offshore Wind Farms Based on Alternating Direction Method of Multipliers (ADMM)
Section V - Fault Ride Through of Wind Farm Clusters
14. Coordinated Droop Control and Adaptive Model Predictive Control for Enhancing HVRT and Post-Event Recovery of Large-Scale Wind Farms
15. Hierarchical Event-Triggered MPC-Based Coordinated Control for HVRT and Voltage Restoration of Large-Scale Wind Farms
16. Coordinated Voltage Support Control for Enhancing LVRT Capability of Large-Scale Wind Farms
Appendix I - Model predictive control basics, methods, and algorithms
Appendix II - Distributed control basics, methods, and algorithms
1. Introduction to Large-Scale Wind Power Integration
Section II - Optimal Active Power Control of Large-Scale Wind Farm Clusters
2. Bi-Level Decentralized Active Power Control for Large-Scale Wind Farm Clusters
3. Optimal Active Power Control Based on MPC for DFIG-based Wind Farm Equipped with Distributed Energy Storage Systems
4. Hierarchical Active Power Control of DFIG-based Wind Farm with Distributed Energy Storage Systems based on Alternating Direction Method of Multipliers (ADMM)
5 Hierarchical Optimal Control for Synthetic Inertial Response of Wind Farm Based on Alternating Direction Method of Multipliers (ADMM)
Section III - Optimal Active and Reactive Power Control of Large-Scale Wind Farm Clusters
6. Bi-Level Decentralized Active and Reactive Power Control for Large-Scale Wind Farm Cluster
7. Two-Tier Combined Active and Reactive Power Control for VSC–HVDC Connected Large-Scale Wind Farm Cluster based on Alternating Direction Method of Multipliers (ADMM)
8. Distributed Optimal Active and Reactive Power Control for Wind Farms Based on ADMM
9. ADMM-based Distributed Active and Reactive Power Control for Regional AC Grids with Wind Farms
Section IV - Optimal Voltage Control of Large-Scale Wind Farm Clusters
10. Distributed Voltage Control based on ADMM for Large Scale Wind Farm Cluster connected to VSC HVDC
11. Distributed Optimal Voltage Control for VSC-HVDC Connected Large-Scale Wind Farm Cluster Based on Analytical Target Cascading Method
12. Adaptive Droop-based Hierarchical Optimal Voltage Control Scheme for VSC-HVDC Connected Offshore Wind Farm
13. Distributed Optimal Voltage Control Strategy for AC Grid with DC Connection and Offshore Wind Farms Based on Alternating Direction Method of Multipliers (ADMM)
Section V - Fault Ride Through of Wind Farm Clusters
14. Coordinated Droop Control and Adaptive Model Predictive Control for Enhancing HVRT and Post-Event Recovery of Large-Scale Wind Farms
15. Hierarchical Event-Triggered MPC-Based Coordinated Control for HVRT and Voltage Restoration of Large-Scale Wind Farms
16. Coordinated Voltage Support Control for Enhancing LVRT Capability of Large-Scale Wind Farms
Appendix I - Model predictive control basics, methods, and algorithms
Appendix II - Distributed control basics, methods, and algorithms
- No. of pages: 400
- Language: English
- Edition: 1
- Published: April 4, 2025
- Imprint: Academic Press
- Paperback ISBN: 9780443292347
- eBook ISBN: 9780443292354
QW
Qiuwei Wu
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.
Affiliations and expertise
Professor, School of Electronics, Electrical Engineering, and Computer Science, Queen’s University Belfast, UKSH
Sheng Huang
Sheng Huang is currently a full Professor in the College of Electrical and Information Engineering, Hunan University, China. He received his M.S. and PhD degrees both from the College of Electrical and Information Engineering, Hunan University, Changsha, China, in 2012 and 2016, respectively. His research interests are in decentralized/distributed optimal operation and coordinated control of power systems with high penetration of wind power, including renewable energy generation, modeling and integration study of wind power, and decentralized/distributed voltage control of integrated wind power systems. Prof. Huang is an Editor of IET Renewable Power Generation and Protection and Control of Modern Power Systems.
Affiliations and expertise
Professor, College of Electrical and Information Engineering, Hunan University, ChinaJW
Juan Wei
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.
Affiliations and expertise
College of Electrical and Information Engineering, Hunan University, ChinaPW
Pengda Wang
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.
Affiliations and expertise
Research Associate, College of Electrical and Information Engineering, Hunan University, ChinaCL
Canbing Li
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.
Affiliations and expertise
Professor, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaVT
Vladimir Terzija
Vladimir Terzija is a Full Professor at the School of Engineering, Newcastle University, in the United Kingdom. He was previously a Full Professor and the Head of Laboratory of Modern Energy Systems with Skoltech, Russian Federation (2021-23), EPSRC Chair Professor in power system engineering at the University of Manchester, UK (2006-20), and an Assistant Professor at the University of Belgrade (1997-99). From 2000 to 2006, he was a Senior Specialist for switchgear and distribution automation with ABB, in Germany. His research interests include smart grid applications, wide-area monitoring, protection and control, multi-energy systems, switchgear and transient processes, ICT, data analytics, and digital signal processing applications in power systems. Prof. Terzija was the recipient of the prestigious Alexander von Humboldt Fellowship, and is the Editor-in-Chief of the International Journal of Electrical Power and Energy Systems.
Affiliations and expertise
Professor, School of Engineering, Newcastle University, UK