03798nam 2200721z- 450 9910367747103321202102113-03921-761-5(CKB)4100000010106246(oapen)https://directory.doabooks.org/handle/20.500.12854/41063(oapen)doab41063(EXLCZ)99410000001010624620202102d2019 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierApplications of Computational Intelligence to Power SystemsMDPI - Multidisciplinary Digital Publishing Institute20191 online resource (116 p.)3-03921-760-7 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.History of engineering and technologybicsscactive distribution systemboiler load constraintsCNNcombined economic emission/environmental dispatchCombustion efficiencydefect detectiondifferential evolution algorithmeconomic load dispatchelectricity load forecastingemission dispatchfeature extractiongenetic algorithmgenetic algorithm (GA)glass insulatorgrid observabilityincipient cable failureinertia weightleast square support vector machinelocalizationlong short term memory (LSTM)meter allocationmodel predictive controlmultivariate time seriesNOx emissions constraintsparameter estimationparticle swarm optimizationparticle update modepenalty factor approachreactive power optimizationself-shatteringshort term load forecasting (STLF)spatial featuresVMDHistory of engineering and technologyKodogiannis Vassilis Sauth1301162BOOK9910367747103321Applications of Computational Intelligence to Power Systems3025744UNINA