Applications of Computational Intelligence to Power Systems

• Format: eBook
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Subjects: History of engineering and technology; active distribution system; boiler load constraints; CNN; combined economic emission/environmental dispatch; Combustion efficiency; defect detection; differential evolution algorithm; economic load dispatch; electricity load forecasting; emission dispatch; feature extraction; genetic algorithm; genetic algorithm (GA); glass insulator; grid observability; incipient cable failure; inertia weight; least square support vector machine; localization; long short term memory (LSTM); meter allocation; model predictive control; multivariate time series; NOx emissions constraints; parameter estimation; particle swarm optimization; particle update mode; penalty factor approach; reactive power optimization; self-shattering; short term load forecasting (STLF); spatial features; VMD
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication
• Physical Description: 1 online resource (116 p.)
• ISBN: 9783039217601; 9783039217618; books978-3-03921-761-8
• Access: Open Access

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.