Artificial Intelligence-based Smart Power Systems - Tapa dura

Acerca del autor

Sanjeevikumar Padmanaban, PhD, is a Full Professor with the Department of Electrical Engineering, IT and Cybernetics, at the University of South-Eastern Norway, Porsgrunn, Norway. He serves as an Editor/Associate Editor/Editorial Board Member of many refereed journals, in particular, the IEEE Systems Journal, the IEEE Access Journal, IEEE Transactions on Industry Applications, the Deputy Editor/Subject Editor of IET Renewable Power Generation, and IET Generation, Transmission and Distribution Journal, Subject Editor of FACETS and Energies MDPI Journal.

Sivaraman Palanisamy is a Program Manager - EV Charging Infrastructure in WRI India. He is an IEEE Senior Member, a Member of CIGRE, and Life Member of the Institution of Engineers (India). He is an active participant in the IEEE Standards Association.

Sharmeela Chenniappan, PhD, is a Professor in the Department of EEE, CEG campus, Anna University, Chennai, India. She is an IEEE Senior Member, a Life Member of CBIP, and Member of the Institution of Engineers (India), ISTE, and SSI.

Jens Bo Holm-Nielsen, PhD, is the Head of the Esbjerg Energy Section with the Department of Energy Technology at Aalborg University. He has been an organizer of various international conferences, workshops, and training programs.

De la contraportada

Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studies

Artificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution. It covers many new topics such as distribution phasor measurement units, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. The text also explores the potential consequences of artificial intelligence and advanced technologies in smart power systems in the forthcoming years.

To enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies.

Artificial Intelligence-based Smart Power Systems includes specific information on topics such as:

• Modeling and analysis of smart power systems, covering steady state analysis, dynamic analysis, voltage stability, and more
• Recent advancement in power electronics for smart power systems, covering power electronic converters for renewable energy sources, electric vehicles, and HVDC/FACTs
• Distribution Phasor Measurement Units (PMU) in smart power systems, covering the need for PMU in distribution and automation of system reconfigurations
• Power and energy management systems

Engineering colleges and universities, along with industry research centers, can use the in-depth subject coverage and the extensive supplementary learning resources found in Artificial Intelligence-based Smart Power Systems to gain a holistic understanding of the subject and be able to harness that knowledge within a myriad of practical applications.