Monitoring and Control of Electrical Power Systems using Machine Learning Techniques

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.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

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