Artificial Intelligence in the Operation and Control of Digitalized Power Systems
Springer International Publishing (Verlag)
978-3-031-69357-1 (ISBN)
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This book covers the practical application of AI-based methods in modern power systems. The complexity of current power system operations has dramatically increased due to the higher penetration of renewable energy sources and power electronic components. Therefore, providing efficient techniques is essential for secure and clean power system operation. This book focuses on the data-driven operation of the digitalized power system using machine language (ML). First, the basics of power system operation and control are presented, covering various areas of system control and operation. Next, significant advances in modern power systems and their corresponding challenges are discussed, and artificial intelligence (AI)-powered techniques, specifically machine learning, are introduced to address these issues. The book also explores AI-powered applications in the operation of power systems. These applications include various aspects of the data-driven process in both situational awareness and control areas. They are presented as practical examples indicating the implementation of an ML-based method to solve operational problems.
Artificial Intelligence in the Operation and Control of Digitalized Power Systems is a valuable guide for students, researchers, and practicing engineers to AI-based techniques and real-world applications in power systems.
Morteza Nazari-Heris, Ph.D., is an Assistant Professor of Electrical Engineering at the Department of Engineering at East Carolina University. Before that, he worked as an Assistant Professor of Electrical Energy Systems with the College of Engineering at Lawrence Technological University and as a Graduate Research Assistant with the Department of Architectural Engineering at Pennsylvania State University, where he earned his Ph.D. specializing in energy systems. During his graduate studies, he worked on projects for the future, flexible, equitable, and robust networks of charging stations for high adoption of electric vehicles, application of machine learning and deep learning methods to energy systems, and sustainable design of buildings with renewable energy sources and energy storage facilities. Dr. Nazari-Heris obtained his BSc and MSc in electrical engineering from the University of Tabriz. His main areas of interest are energy system operation, energy management, sustainability, zero-energy Buildings, electric vehicles, microgrids, multi-carrier energy systems, and energy storage technologies. He has received several awards and fellowships, including three Outstanding Thesis Awards. He serves as an editor and reviewer for several journals and conferences. He is an active member of several professional communities, including the IEEE, the Clean Energy Leadership Institute (CELI), and Young Professionals in Energy.
Sasan Azad is a Ph.D. student in the Department of Electrical Engineering and a researcher at the Electrical Networks Institute of the Shahid Beheshti University. He obtained his BSc degree from the Razi University of Kermanshah and his MSc from the Shahid Beheshti University. His main areas of interest are power system security and voltage stability, smart grids, and electric vehicles.
Constant and Product-Cubic Systems.- Self-linear and Product-cubic systems.- Crossing-linear and Product-cubic systems.
| Erscheinungsdatum | 17.11.2024 |
|---|---|
| Reihe/Serie | Power Systems |
| Zusatzinfo | VI, 400 p. 143 illus., 112 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| Schlagworte | AI Applications in Power Systems • Cyber-physical Systems (CPS) • Demand Side Management • Digitalized Power Systems • Electricity pricing • Fault Diagnosis in Power Systems • load forecasting • Machine Learning in Power System Operation • Modern Power Systems • Power System Operation and Control • Renewable Generation • State Estimation • Surrogate Control Solutions |
| ISBN-10 | 3-031-69357-4 / 3031693574 |
| ISBN-13 | 978-3-031-69357-1 / 9783031693571 |
| Zustand | Neuware |
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