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Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field.

localization --- reactive power optimization --- model predictive control --- CNN --- long short term memory (LSTM) --- meter allocation --- particle update mode --- combined economic emission/environmental dispatch --- glass insulator --- emission dispatch --- genetic algorithm --- grid observability --- defect detection --- feature extraction --- parameter estimation --- incipient cable failure --- active distribution system --- boiler load constraints --- multivariate time series --- particle swarm optimization --- inertia weight --- VMD --- NOx emissions constraints --- spatial features --- penalty factor approach --- self-shattering --- differential evolution algorithm --- short term load forecasting (STLF) --- genetic algorithm (GA) --- economic load dispatch --- least square support vector machine --- Combustion efficiency --- electricity load forecasting