Applications of Computational Intelligence to Power Systems

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...

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Bibliographic Details
Main Author: Kodogiannis, Vassilis S. (auth)
Format: Electronic Book Chapter
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
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Online Access:DOAB: download the publication
DOAB: description of the publication
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520 |a 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. 
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650 7 |a History of engineering & technology  |2 bicssc 
653 |a localization 
653 |a reactive power optimization 
653 |a model predictive control 
653 |a CNN 
653 |a long short term memory (LSTM) 
653 |a meter allocation 
653 |a particle update mode 
653 |a combined economic emission/environmental dispatch 
653 |a glass insulator 
653 |a emission dispatch 
653 |a genetic algorithm 
653 |a grid observability 
653 |a defect detection 
653 |a feature extraction 
653 |a parameter estimation 
653 |a incipient cable failure 
653 |a active distribution system 
653 |a boiler load constraints 
653 |a multivariate time series 
653 |a particle swarm optimization 
653 |a inertia weight 
653 |a VMD 
653 |a NOx emissions constraints 
653 |a spatial features 
653 |a penalty factor approach 
653 |a self-shattering 
653 |a differential evolution algorithm 
653 |a short term load forecasting (STLF) 
653 |a genetic algorithm (GA) 
653 |a economic load dispatch 
653 |a least square support vector machine 
653 |a Combustion efficiency 
653 |a electricity load forecasting 
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