Includes bibliographical references and index.

Derivation of generic equivalent models for distributin network analysis using artificial intelligence techniques / Eleftherios O. Kontis, Theogilos A. Papadopoulos, Maheruddin H. Syed, Grigoris K. Papagiannis -- Distubance dataset development for machine-learning-based power quality monitoring in distributed generation system: a practical guide / Oswaldo Isaac Cortes Robles, Emilio Barocio Espejo, Juan Segundo Ramírez, Julio Cesar Hernández Ramírez -- Advances in compression algorithms for PMU and smart meter data based on tensor decomposition / Betsy Sandoval Guzmán, Emilio Baocio Espejo, Petr Korba, Felix Rafael Segundo Sevilla.

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.