Monitoring and Control of Electrical Power Systems using Machine Learning Techniques

Monitoring and Control of Electrical Power Systems using Machine Learning Techniques

1st Edition - January 12, 2023

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  • Editors: Emilio Barocio Espejo, Felix Rafael Segundo Sevilla, Petr Korba
  • Paperback ISBN: 9780323999045

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Description

Monitoring and Control of Electrical Power Systems using Machine Learning Techniques bridges the gap between advanced machine learning techniques and their application in the control and monitoring of electrical power systems, particularly as relevant for heavily distributed energy systems and real-time application. It reviews key applications of deep learning, spatio-temporal and advanced signal processing methods for the monitoring of power quality. It introduces guiding principles for the monitoring and control of power quality disturbances arising from integration of power electronic devices. The work discusses the monitoring and control of electrical power systems using benchmark test systems for the creation of bespoke advanced data analytic algorithms. The last part of the book evaluates applications of these technologies, including anomaly detection and real-time classification of disturbances to track the propagation and localization of the source of the disturbance, visualization tools, load modelling and fault location estimation.

Key Features

  • Covers advanced applications and solutions for monitoring and control of electrical power systems using machine learning techniques for transmission and distribution systems
  • Provides deep insight into power quality disturbance detection and classification through machine learning, deep learning and spatio-temporal algorithms
  • Strengthened by heavy online supplementary components focusing on dataset generation for machine learning training processes and open-source microgrid model simulators on GitHub

Readership

Early career researchers at 1st year PhD, postdoctoral and at professor level seeking to apply advanced methods to monitor and optimize power quality using ML; Engineers in utilities and energy producers seeking monitoring methodologies and new ways to face power system constraints

Table of Contents

  • 1. Introduction to Monitoring and control of electrical power systems using machine learning techniques
    2. Power quality disturbances in electrical power systems
    3. Monitoring and control in electrical power systems
    4. Benchmark Test Systems for the Validation of Power Quality Disturbance Studies
    5. Advanced signal processing methods for monitoring and control of Electrical Power Systems
    6. Monitoring of Electrical Power Systems based on Automatic Learning methods
    7. Spatio-Temporal Data-Driving Methods for Monitoring of Electrical Power Systems
    8. Data Analytic Applications for Monitoring of Electrical Power Systems
    9. Trends in Monitoring and Control of Power Quality in Electrical Power Systems
    10. Didactic examples of algorithm implementation

Product details

  • No. of pages: 296
  • Language: English
  • Copyright: © Elsevier 2023
  • Published: January 12, 2023
  • Imprint: Elsevier
  • Paperback ISBN: 9780323999045

About the Editors

Emilio Barocio Espejo

Emilio Barocio Espejo received the Ph.D. degree from CINVESTAV, Guadalajara, in 2003, in electrical engineering. He is a full Professor at the Graduate Program forElectrical Engineering and Data Science of the University of Guadalajara. Dr. Barocio was a recipient of the Arturo Rosenblueth Award for the best Ph.D. thesis on Science and Technology of México in 2003. He was distinguished with the Marie-Curie Incoming International Fellowship at Imperial College London in 2013. He was also a recipient of the IEEE Power and Energy Society and the IEEE Power System Dynamic Performance Committee Prize Paper Awards, both in 2018. His research interests focus on the integration of data analytics in power system monitoring. In the last 10 years his main aims have been to aid the development and application of methods drawing from spatio-temporal data driven, machine learning, data mining and meta heuristic optimization.

Affiliations and Expertise

Professor of Electrical Engineering and Data Science, University of Guadalajara, Mexico

Felix Rafael Segundo Sevilla

Felix Rafael Segundo Sevilla received his PhD degree from Imperial College London, United Kingdom in 2013. From January 2013 to July 2014, Dr Segundo was a postdoctoral research fellow at the KTH Royal Institute of Technology in Stockholm, Sweden. Since 2014, he has been a Research Associate in the Zurich University of Applied Science ZHAW, Switzerland. Dr Segundo was awarded with an Ambizione Energy grant from the Swiss National Science Foundation (SNSF) to conduct his own research project entitled “Stability Assessment of Forthcoming Power Networks with Massive Integration of Renewable Energy Sources” for the period 2018-2021. Dr Segundo is a Senior Member of the IEEE, chair of the annual international workshop DynPOWER and chair of the IEEE task force " Application of Big Data Analytics on Transmission System Dynamic Security Assessment".

Affiliations and Expertise

Research Associate, Zurich University of Applied Science, ZHAW, Zurich, Switzerland

Petr Korba

Petr Korba received his Dr.-Ing. degree from the University of Duisburg, Germany in 1999. He worked for more than 10 years as a principal scientist at ABB Corporate Research. He became a professor of electric power systems at the ZHAW and deputy head of the institute of energy systems in 2012 and 2015, respectively. Dr Korba has published over 100 articles in international journals and at international conferences in the field of automatic control and electric power systems. He has authored and co-authored over 100 US and European patents and patent applications and was nominated for the Best European Patent Award in 2011 for his achievements in the wide-area monitoring and control of electric power systems.

Affiliations and Expertise

Professor of Electric Power Systems, ZHAW, Deputy Head of the Institute of Energy Systems, Zurich, Switzerland

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