Monitoring and Control of Electrical Power Systems using Machine Learning Techniques

Erscheinungsjahr 2023
ISBN: 978-0-323-99904-5
Verlag: Elsevier - Health Sciences Division

Buch, Englisch, 296 Seiten, Format (B × H): 227 mm x 151 mm, Gewicht: 584 g

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.

Autoren/Hrsg.

Barocio Espejo, Emilio

Segundo Sevilla, Felix Rafael

Korba, Petr

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik

Inhaltsverzeichnis

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

Über Autor(innen)

Korba, Petr
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.

Barocio Espejo, Emilio
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.