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. Monitoring and Control of Electrical Power Systems Using Machine Learning Techniques - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. Contributors - p. Page v 8. Preface - p. Page vii 9. List of Illustrations - p. Page x 10. List of Tables - p. Page xi 11. 1 : Derivation of generic equivalent models for distribution network analysis using artificial intelligence techniques - p. Page xi 12. 2 : Disturbance dataset development for machine-learning-based power quality monitoring in distributed generation systems: a practical guide - p. Page 23 13. 3 : Advances in compression algorithms for PMU and Smart Meter data based on tensor decomposition - p. Page 48 14. 4 : Machine learning and digital twins: monitoring and control for dynamic security in power systems - p. Page 75 15. 5 : Synchrophasor applications in distribution systems: real-life experience - p. Page 103 16. 6 : A graph mapping based supervised machine learning strategy for PMU voltage anomalies' detection and classification in distribution networks - p. Page 134 17. 7 : Identification of source harmonics in electrical networks using spatiotemporal approaches - p. Page 160 18. 8 : Power quality harmonic monitoring by the O-splines-based multiresolution signal decomposition - p. Page 187 19. 9 : Monitoring system for identifying power quality issues in distribution networks using Petri nets and Prony method - p. Page 215 20. 10 : Dynamic voltage restorer controlled per independent phases for power quality sags-swells mitigation under unbalanced conditions - p. Page 241 21. 11 : AI application for load forecasting: a comparison of classical and deep learning methodologies - p. Page 258 22. 12 : Study of harmonics in linear, nonlinear nonsinusoidal electrical circuits by geometric algebra - p. Page 284 23. 13 : Harmonic sources estimation in distribution systems - p. Page 306 24. Index - p. Page 326
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. Monitoring and Control of Electrical Power Systems Using Machine Learning Techniques - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. Contributors - p. Page v 8. Preface - p. Page vii 9. List of Illustrations - p. Page x 10. List of Tables - p. Page xi 11. 1 : Derivation of generic equivalent models for distribution network analysis using artificial intelligence techniques - p. Page xi 12. 2 : Disturbance dataset development for machine-learning-based power quality monitoring in distributed generation systems: a practical guide - p. Page 23 13. 3 : Advances in compression algorithms for PMU and Smart Meter data based on tensor decomposition - p. Page 48 14. 4 : Machine learning and digital twins: monitoring and control for dynamic security in power systems - p. Page 75 15. 5 : Synchrophasor applications in distribution systems: real-life experience - p. Page 103 16. 6 : A graph mapping based supervised machine learning strategy for PMU voltage anomalies' detection and classification in distribution networks - p. Page 134 17. 7 : Identification of source harmonics in electrical networks using spatiotemporal approaches - p. Page 160 18. 8 : Power quality harmonic monitoring by the O-splines-based multiresolution signal decomposition - p. Page 187 19. 9 : Monitoring system for identifying power quality issues in distribution networks using Petri nets and Prony method - p. Page 215 20. 10 : Dynamic voltage restorer controlled per independent phases for power quality sags-swells mitigation under unbalanced conditions - p. Page 241 21. 11 : AI application for load forecasting: a comparison of classical and deep learning methodologies - p. Page 258 22. 12 : Study of harmonics in linear, nonlinear nonsinusoidal electrical circuits by geometric algebra - p. Page 284 23. 13 : Harmonic sources estimation in distribution systems - p. Page 306 24. Index - p. Page 326
