This book focuses on two of the most important aspects of wind farm operation: decisions and control. The first part of the book deals with decision-making processes, and explains that hybrid wind farm operation is governed by a set of alternatives that the wind farm operator must choose from in order to achieve optimal delivery of wind power to the utility grid. This decision-making is accompanied by accurate forecasts of wind speed, which must be known beforehand. Errors in wind forecasting can be compensated for by pumping power from a reserve capacity to the grid using a battery energy storage system (BESS). Alternatives based on penalty cost are assessed using certain criteria, and MCDM methods are used to evaluate the best choice. Further, considering the randomness in the dynamic phenomenon in wind farms, a fuzzy MCDM approach is applied during the decision-making process to evaluate the best alternative for hybrid wind farm operation. Case studies from wind farms in theUSA are presented, together with numerical solutions to the problem.
In turn, the second part deals with the control aspect, and especially with yaw angle control, which facilitates power maximization at wind farms. A novel transfer function-based methodology is presented that controls the wake center of the upstream turbine(s); lidar-based numerical simulation is carried out for wind farm layouts; and an adaptive control strategy is implemented to achieve the desired yaw angle for upstream turbines. The proposed methodology is tested for two wind farm layouts. Wake management is also implemented for hybrid wind farms where BESS life enhancement is studied. The effect of yaw angle on the operational cost of BESS is assessed, and case studies for wind farm datasets from the USA and Denmark are discussed. Overall, the book provides a comprehensive guide to decision and control aspects for hybrid wind farms, which are particularly important from an industrial standpoint.
Fundamentals of Wind Turbine and Wind Farm Control
Systems.- Multi-Criteria Decision Making: An Overview.- Decision Making in
Hybrid Wind Farms.- Fuzzy based Decision Making in Hybrid Wind
Farms.- Control Applications in Hybrid Wind Farms.- BESS Life Enhancement for
Hybrid Wind Farms.
Harsh S Dhiman is currently pursuing his Ph.D. at the Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management (IITRAM), Ahmedabad, India. He obtained his Masters degree in Electrical Power Engineering from the Faculty of Technology & Engineering, Maharaja Sayajirao University of Baroda, Vadodara, India, and his B.Tech. in Electrical Engineering from the Institute of Technology, Nirma University, Ahmedabad, India. His research interests include hybrid operation of wind farms, hybrid wind forecasting techniques, and wake management at wind farms.
Dipankar Deb received his Ph.D. from the University of Virginia, Charlottesville, under the supervision of Prof. Gang Tao, in 2007. In 2017, he was elected to be an IEEE Senior Member. He has served as a Lead Engineer at GE Global Research, Bengaluru (20122015) and as an Assistant Professor of Electrical Engineering at the IIT Guwahati (20102012). Presently, he is a Professor ofElectrical Engineering at the Institute of Infrastructure Technology Research and Management (IITRAM), Ahmedabad, India. His research interests include control theory, stability analysis, and renewable energy systems.
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