Monitoring and Control of Electrical Power Systems using Machine Learning Techniques
Language: English
Published by Elsevier - Health Sciences Division Jan 2023, 2023
- Softcover
- New
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
AbeBooks seller since January 11, 2012
Condition: New
£ 127.11
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Add to basketItem description from seller
This item is printed on demand - it takes 3-4 days longer - Neuware -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 relevant for heavily distributed energy systems and real-time application. The book reviews key applications of deep learning, spatio-temporal, and advanced signal processing methods for monitoring power quality. This reference introduces guiding principles for the monitoring and control of power quality disturbances arising from integration of power electronic devices and discusses monitoring and control of electrical power systems using benchmark test systems for the creation of bespoke advanced data analytic algorithms. 352 pp. Englisch.
Seller Inventory # 9780323999045
- Title
- Monitoring and Control of Electrical Power Systems using Machine Learning Techniques
- Author
- Emilio Barocio Espejo
- Publisher
- Elsevier - Health Sciences Division Jan 2023
- Publication year
- 2023
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0323999042
- ISBN 13
- 9780323999045
- Item weight
- 584 grams
- Dimensions
- 227x151x21 mm
- 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
- Includes substantial online supplementary components focusing on dataset generation for machine learning training processes and open-source microgrid model simulators on GitHub
"Synopsis" may belong to another edition of this title.
About the Author
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".
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.
"About the title" may belong to another edition of this title.
BuchWeltWeit Ludwig Meier e.K.
Bergisch Gladbach, Germany
AbeBooks seller since January 11, 2012
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