Monitoring and Control of Electrical Power Systems using Machine Learning Techniques

Reviews key applications of deep learning, spatio-temporal, and advanced signal processing methods for optimizing power quality monitoring and control

Emilio Barocio Espejo (Edited by), Felix Rafael Segundo Sevilla (Edited by), Petr Korba (Edited by)

9780323999045, Elsevier Science

Paperback / softback, published 23 January 2023

352 pages, Approx. 400 illustrations
22.9 x 15.2 x 2.3 cm, 0.57 kg

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.

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

Subject Areas: Electric motors [THRM]