INTELLIGENT OPTIMIZATION IN ELECTRIC MACHINES, RENEWABLE ENERGY SYSTEMS, AND MICROGRIDS: METAHEURISTIC APPROACHES TO MODELLING, CONTROL, AND FAULT DIAGNOSIS

This monograph is a research compendium on the modelling, control, fault diagnosis, and optimization of electric machines, renewable energy systems, and microgrids using advanced computational methods and metaheuristic algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO) algorithms, artificial bee colony (ABC) algorithms, runner root (RR) algorithms, and cuckoo search (CS) algorithms.

Intelligent Optimization in Electric Machines, Renewable Energy Systems, and Microgrids offers in-depth analysis of core components, including parameter estimation and energy-efficient control of induction machines, along with the modelling and maximum power point tracking (MPPT) of solar photovoltaic (PV) and wind energy systems. The book also covers modern techniques for fault location in transmission lines, the optimization of hybrid renewable energy systems, and the planning and control of microgrids.

Designed for power systems engineers, researchers, academics, and students, this book offers the practical knowledge and advanced methodologies needed to address the most pressing challenges in a modern grid. It is an ideal textbook for graduate courses in electric power systems, renewable energy systems, and microgrids, providing both theoretical foundations and real-world applications.

Duy C Huynh (Senior Member, IEEE) received his BE and ME degrees in Electrical Engineering from the University of Technology, Vietnam National University of Ho Chi Minh City, Ho Chi Minh City, Vietnam, in 2001 and 2005, respectively, and his PhD degree in Electrical Engineering from Heriot-Watt University, Edinburgh, United Kingdom, in 2010. He is an Associate Professor in Electrical Engineering at HUTECH University, Ho Chi Minh City, Vietnam, and is currently affiliated with Louisiana State University, Baton Rouge, Louisiana, United States. His research interests include the areas of energy-efficient control and parameter estimation for induction machine drive systems, power systems, and renewable energy systems; operation optimization in microgrids and smart grids; fault location on transmission lines; and artificial intelligence applications in electrical engineering, education, health, and agriculture.

Loc D Ho received his B.Sc. degree in Electrical Engineering from the National Technical University, Kharkiv Polytechnic Institute, Ukraine, in 1991; D.Eng. degree in Automation Engineering from the National Technical University of Ukraine, Igor Sikorsky Kyiv Polytechnic Institute, Ukraine, in 1994; and Dr.Sc. degree in Automation Engineering from the National Research University, Moscow Power Engineering Institute, Russia, in 2002. He is currently a Professor and the President of HUTECH University, Vietnam. His research interests include control and operation in power systems, microgrids and smart grids, stability analysis in control systems, soft computing techniques such as neural networks and fuzzy logics, meta-heuristic algorithms such as genetic algorithms and particle swarm optimization algorithms, and cloud computing techniques.

Matthew W Dunnigan received his B.Sc. degree in electrical and electronic engineering (with First-Class Hons.) from Glasgow University, Glasgow, UK, in 1985, and M.Sc. and PhD degrees from Heriot-Watt University, Edinburgh, UK, in 1989 and 1994, respectively. From 1985 to 1988, he was a Development Engineer in Ferranti, specializing in the design of power supplies and control systems for moving optical assemblies and device temperature stabilisation. In 1989, he became a Lecturer at Heriot-Watt University, where his work centered on the evaluation and reduction of the dynamic coupling between a robotic manipulator and an underwater vehicle. He is currently an Associate Professor, where his research grants and interests are in the areas of hybrid position/force control of an underwater manipulator, coupled control of manipulator-vehicle systems, nonlinear position/speed control and parameter estimation methods in vector control of induction machines, frequency domain self-tuning/adaptive filter control methods for random vibration, and shock testing using electrodynamic actuators.

Corina Barbalata (Member, IEEE) received her B.E. degree in electrical engineering from Transilvania University, Brasov, Romania, in 2011; M.Sc. degree in computer vision and robotics, jointly from Heriot-Watt University, Edinburgh, United Kingdom and University of Burgundy, Dijon, France, in 2013; PhD degree in electrical engineering from Heriot-Watt University, Edinburgh, United Kingdom, in 2017. She is currently an Assistant Professor at Louisiana State University, Baron Rouge, Louisiana, United States. Her research interests include marine robot systems, autonomous mobile-manipulation, cooperative robotics, multi-body control and planning theory, and underwater perception systems and algorithms.