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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 as relevant for heavily distributed energy systems and real-time application. It reviews key applications of deep learning, spatio-temporal and advanced signal processing methods for the monitoring of power quality. It introduces guiding principles for the monitoring and control of power quality disturbances arising from integration of power electronic devices. The work discusses the monitoring and control of electrical power systems using benchmark test systems for the creation of bespoke advanced data analytic algorithms. The last part of the book evaluates applications of these technologies, including anomaly detection and real-time classification of disturbances to track the propagation and localization of the source of the disturbance, visualization tools, load modelling and fault location estimation. 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 Strengthened by heavy online supplementary components focusing on dataset generation for machine learning training processes and open-source microgrid model simulators on GitHub
