Application of Machine Learning and Deep Learning Methods to Power System Problems /

This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider predic...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Nazari-Heris, Morteza (Editor), Asadi, Somayeh (Editor), Mohammadi-Ivatloo, Behnam (Editor), Abdar, Moloud (Editor), Jebelli, Houtan (Editor), Sadat-Mohammadi, Milad (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition:1st ed. 2021.
Series:Power Systems,
Subjects:
Online Access:Connect to the full text of this electronic book
Description
Summary:This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses. Offers innovative machine learning and deep learning methods for dealing with power system issues; Provides promising solution methodologies; Covers theoretical background and experimental analysis.
Physical Description:1 online resource (IX, 391 pages 120 illustrations)
ISBN:9783030776961
ISSN:1860-4676
DOI:10.1007/978-3-030-77696-1