Monitoring and Control of Electrical Power Systems using Machine Learning Techniques

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. 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 INDICE: 1. Introduction to Monitoring and control of electrical power systems using machine learning techniques 2. Power quality disturbances in electrical power systems3. Monitoring and control in electrical power systems4. Benchmark Test Systems for the Validation of Power Quality Disturbance Studies5. Advanced signal processing methods for monitoring and control of Electrical Power Systems6. Monitoring of Electrical Power Systems based on Automatic Learning methods7. Spatio-Temporal Data-Driving Methods for Monitoring of Electrical Power Systems8. Data Analytic Applications for Monitoring of Electrical Power Systems9. Trends in Monitoring and Control of Power Quality in Electrical Power Systems 10. Didactic examples of algorithm implementation

• ISBN: 9780323999045
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 296
• Fecha Publicación: 27/01/2023
• Nº Volúmenes: 1
• Idioma: Inglés