LEADER 03667nam 2200685z- 450 001 9910367747103321 005 20231214133001.0 010 $a3-03921-761-5 035 $a(CKB)4100000010106246 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/41063 035 $a(EXLCZ)994100000010106246 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplications of Computational Intelligence to Power Systems 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 electronic resource (116 p.) 311 $a3-03921-760-7 330 $aElectric 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. 610 $alocalization 610 $areactive power optimization 610 $amodel predictive control 610 $aCNN 610 $along short term memory (LSTM) 610 $ameter allocation 610 $aparticle update mode 610 $acombined economic emission/environmental dispatch 610 $aglass insulator 610 $aemission dispatch 610 $agenetic algorithm 610 $agrid observability 610 $adefect detection 610 $afeature extraction 610 $aparameter estimation 610 $aincipient cable failure 610 $aactive distribution system 610 $aboiler load constraints 610 $amultivariate time series 610 $aparticle swarm optimization 610 $ainertia weight 610 $aVMD 610 $aNOx emissions constraints 610 $aspatial features 610 $apenalty factor approach 610 $aself-shattering 610 $adifferential evolution algorithm 610 $ashort term load forecasting (STLF) 610 $agenetic algorithm (GA) 610 $aeconomic load dispatch 610 $aleast square support vector machine 610 $aCombustion efficiency 610 $aelectricity load forecasting 700 $aKodogiannis$b Vassilis S$4auth$01301162 906 $aBOOK 912 $a9910367747103321 996 $aApplications of Computational Intelligence to Power Systems$93025744 997 $aUNINA