Applications of Computational Intelligence to Power Systems

Edited by
November 2019
116 pages
  • ISBN978-3-03921-760-1 (Paperback)
  • ISBN978-3-03921-761-8 (PDF)

This book is a reprint of the Special Issue Applications of Computational Intelligence to Power Systems that was published in

Chemistry & Materials Science
Environmental & Earth Sciences
Physical Sciences

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.

  • Paperback
© 2020 by the authors; CC BY license
defect detection; glass insulator; localization; self-shattering; spatial features; particle swarm optimization; particle update mode; inertia weight; reactive power optimization; Combustion efficiency; NOx emissions constraints; boiler load constraints; least square support vector machine; differential evolution algorithm; model predictive control; incipient cable failure; VMD; feature extraction; CNN; economic load dispatch; emission dispatch; combined economic emission/environmental dispatch; particle swarm optimization; genetic algorithm; penalty factor approach; long short term memory (LSTM); genetic algorithm (GA); short term load forecasting (STLF); electricity load forecasting; multivariate time series; grid observability; active distribution system; meter allocation; parameter estimation