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

Morteza Nazari Heris; Somayeh Asadi; Behnam Mohammadi-Ivatloo; Moloud Abdar; Houtan Jebelli; Milad Sadat-Mohammadi

doi:10.1007/978-3-030-77696-1

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

Morteza Nazari Heris (Editor), Somayeh Asadi (Editor), Behnam Mohammadi-Ivatloo (Editor), Moloud Abdar (Editor), Houtan Jebelli (Editor), Milad Sadat-Mohammadi (Editor)

Research output: Book/Report/Conference proceeding › Book

Abstract

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.

Original language	English (US)
Publisher	Springer
Number of pages	391
ISBN (Electronic)	9783030776961
ISBN (Print)	9783030776954, 9783030776985
DOIs	https://doi.org/10.1007/978-3-030-77696-1
State	Published - 2021
Externally published	Yes

Publication series

Name	Power Systems
ISSN (Print)	1612-1287
ISSN (Electronic)	1860-4676

Keywords

Artificial neural networks (ANNs)
Power systems modeling
Power systems
Power system planning
Electrical power engineering
Long short term memory (LSTM)
Convolutional neural network (CNN)
Deep neural network (DNN)
Fault/Event Detection
Evolutionary-based methods
Fuzzy systems
Expert systems

Online availability

10.1007/978-3-030-77696-1

Library availability

Discover UIUC Full Text

1 Chapter
1 Foreword/postscript

Application of Machine Learning for Predicting User Preferences in Optimal Scheduling of Smart Appliances
Sadat-Mohammadi, M., Nazari-Heris, M., Ameli, A., Asadi, S., Mohammadi-Ivatloo, B. & Jebelli, H., 2021, Application of Machine Learning and Deep Learning Methods to Power System Problems. Nazari-Heris, M., Asadi, S., Mohammadi-Ivatloo, B., Abdar, M., Jebelli, H. & Sadat-Mohammadi, M. (eds.). Springer, p. 345-355 11 p. (Power Systems).
Research output: Chapter in Book/Report/Conference proceeding › Chapter
Preface
Nazari-Heris, M., Asadi, S., Mohammadi-Ivatloo, B., Abdar, M., Jebelli, H. & Sadat-Mohammadi, M., 2021, Application of Machine Learning and Deep Learning Methods to Power System Problems. Nazari-Heris, M., Asadi, S., Mohammadi-Ivatloo, B., Abdar, M., Jebelli, H. & Sadat-Mohammadi, M. (eds.). Springer, p. v-vi (Power Systems).
Research output: Chapter in Book/Report/Conference proceeding › Foreword/postscript

Cite this

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title = "Application of Machine Learning and Deep Learning Methods to Power System Problems",

abstract = "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. ",

keywords = "Artificial neural networks (ANNs), Power systems modeling, Power systems, Power system planning, Electrical power engineering, Long short term memory (LSTM), Convolutional neural network (CNN), Deep neural network (DNN), Fault/Event Detection, Evolutionary-based methods, Fuzzy systems, Expert systems",

editor = "Heris, {Morteza Nazari} and Somayeh Asadi and Behnam Mohammadi-Ivatloo and Moloud Abdar and Houtan Jebelli and Milad Sadat-Mohammadi",

year = "2021",

doi = "10.1007/978-3-030-77696-1",

language = "English (US)",

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A2 - Jebelli, Houtan

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AB - 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.

KW - Artificial neural networks (ANNs)

KW - Power systems modeling

KW - Power systems

KW - Power system planning

KW - Electrical power engineering

KW - Long short term memory (LSTM)

KW - Convolutional neural network (CNN)

KW - Deep neural network (DNN)

KW - Fault/Event Detection

KW - Evolutionary-based methods

KW - Fuzzy systems

KW - Expert systems

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BT - Application of Machine Learning and Deep Learning Methods to Power System Problems

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

Abstract

Publication series

Keywords

Online availability

Library availability

Fingerprint

Research output

Application of Machine Learning for Predicting User Preferences in Optimal Scheduling of Smart Appliances

Preface

Cite this