Artificial Intelligence in the Operation and Control of Digitalized Power Systems

Author:   Sasan Azad ,  Morteza Nazari-Heris
Publisher:   Springer International Publishing AG
Edition:   2024 ed.
ISBN:  

9783031693571


Pages:   400
Publication Date:   16 November 2024
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Artificial Intelligence in the Operation and Control of Digitalized Power Systems


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Author:   Sasan Azad ,  Morteza Nazari-Heris
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2024 ed.
ISBN:  

9783031693571


ISBN 10:   3031693574
Pages:   400
Publication Date:   16 November 2024
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Part 1: A Conceptual Introduction to the Operation and Control of Digitalized Power Systems.- Basics of Power Systems Operation and Controls.- Challenges and Issues in Modern Power Systems.- Introduction to Artificial Intelligence Applications in Power Systems.- Current Trends and New Perspectives for the Application of Machine Learning in Future Power System Operation.- Part 2: Artificial Intelligence in Operation and Control of Digitalized Energy Systems.- Data Recovery in the Modern Power Systems.- Short-term Forecasting in the Modern Power Systems: Electricity Price, Load, Renewable Generation.- Data-Driven Security and Stability Assessment in the Power Electronics-based Modern Power Systems.- Fault Diagnose in the Modern Power Systems.- Anomaly Detection in Modern Cyber-Physical Systems.- Integrated Condition-based Maintenance Management for Main Equipment of Power Systems Using AI.- AI-based State Estimation in the Modern Power Systems.- AI-based Demand Side Management in theModern Power Systems.- AI-based Surrogate Control Solutions in the Modern Power Systems.- Appendix: Tutorial for ML Applications in the Power System Using Python. 

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Author Information

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.  

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