Advanced Control and Optimization Paradigms for Wind Energy Systems

This book presents advanced studies on the conversion efficiency, mechanical reliability, and the quality of power related to wind energy systems. The main concern regarding such systems is reconciling the highly intermittent nature of the primary source (wind speed) with the demand for high-quality electrical energy and system stability. This means that wind energy conversion within the standard parameters imposed by the energy market and power industry is unachievable without optimization and control. The book discusses the rapid growth of control and optimization paradigms and applies them to wind energy systems: new controllers, new computational approaches, new applications, new algorithms, and new obstacles.

Autorentext
Radu-Emil Precup received his Dipl.Ing. degree in Automation and Computers from the Traian Vuia Polytechnic Institute of Timisoara, Romania, in 1987, his diploma in Mathematics from the West University of Timisoara in 1993, and his Ph.D. degree in Automated Systems from the Politehnica University of Timisoara in 1996, where he is currently working. He has been an Editor-in-Chief of the International Journal of Artificial Intelligence since 2008 and also serves on the editorial boards of several other prestigious journals including Applied Soft Computing (Elsevier), Evolving Systems (Springer) and Cogent Engineering (Taylor & Francis). He is the author/coauthor of more than 300 papers published in various scientific journals, conference proceedings, and book chapters. His research interests mainly include the development and analysis of new control structures and algorithms, theory and applications of soft computing, computer-aided design of control systems, and applications to mechatronic systems (including automotive systems and mobile robots). Among other achievements, he was listed as one of the top 10 researchers in Artificial Intelligence and Automation (IIoT World July 2017). Tariq Kamal holds doctorates in Energy and Sustainability Engineering from the University of Cadiz, Spain; and in Electrical Power and Control Engineering from Sakarya University, Turkey. His research interests include advanced intelligent control techniques for power systems; hybrid renewable energy systems; plug-in hybrid electric vehicles and their integration in power systems; distributed generation; smart grid applications; and the application of signal processing in power systems. Currently, he is working with the research group on Electrical Technologies for Sustainable and Renewable Energy, University of Cadiz, Spain. He is the author/co-author of more than 50 papers published in various international scientific journals and conference proceedings. He has been involved in major joint projects around the globe, including Spain, Turkey, Australia, China, the UK, Pakistan and Estonia, and has been a regular reviewer for many leading international journals, including IEEE Transactions on Industrial Electronics, IEEE access, and Applied Energy. Syed Zulqadar Hassan received his Ph.D. degree in Electrical Engineering from Chongqing University, China. His research interests include fuzzy logic, neural networks, optimal control, adaptive control, integration of renewable energies, smart grids and the application of signal processing (wavelets) in power systems. He has contributed more than 45 publications in the field of Electrical Engineering, and frequently serves as a reviewer for IEEE journals and international conferences.

Inhalt
Nonlinear Modeling, Analysis and Simulation of Wind Turbine Control System With and Without Pitch Control as in Industry.- Distributed Cooperative Control of Wind Farms with On-site Battery Energy Storage Systems.- Sensitivity Analysis of Frequency Regulation Parameters in Power Systems with Wind Generation.- Wind Turbines Integration into Power Systems: Advanced Control Strategy for Harmonics Mitigation.- Power Conversion and Predictive Control of Wind Energy Conversion Systems.- Adaptive Guaranteed Performance Control of Wind Energy Systems.- Machine Learning and Meta-heuristic Algorithms For Renewable Energy: A Systematic Review.- Design of a supervisory control system based on fuzzy logic for a hybrid system comprising wind power, battery and ultracapacitor energy storage system.- Neural-based P-Q Decoupled Control for Doubly Fed Induction Generator in Wind Generation System.- An Indirect Adaptive Control Paradigm for Wind Generation Systems.