LEADER 04296nam 22006495 450 001 9910392750103321 005 20200703003331.0 010 $a981-15-4004-7 024 7 $a10.1007/978-981-15-4004-2 035 $a(CKB)4100000011034843 035 $a(DE-He213)978-981-15-4004-2 035 $a(MiAaPQ)EBC6167061 035 $a(PPN)243759681 035 $a(EXLCZ)994100000011034843 100 $a20200407d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNature Inspired Optimization for Electrical Power System /$fedited by Manjaree Pandit, Hari Mohan Dubey, Jagdish Chand Bansal 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (XIV, 129 p. 49 illus., 35 illus. in color.) 225 1 $aAlgorithms for Intelligent Systems,$x2524-7565 311 $a981-15-4003-9 320 $aIncludes bibliographical references. 327 $aTeaching Learning Based Optimization for Static and Dynamic Load Dispatch -- Application of Elitist Teacher Learner Based Optimization Algorithm for Congestion Management -- PSO Based Optimization of Levelized Cost of Energy for Hybrid Renewable Energy System -- PSO Based PID Controller Designing for LFC of Single Area Electrical Power Network -- Combined Economic Emission Dispatch of Hybrid Thermal-PV System Using Artificial Bee Colony Optimization -- Dynamic Scheduling of Energy Resources in Microgrid Using Grey Wolf Optimization -- Short-Term Hydrothermal Scheduling Using Bio- Inspired Computing: A Review. 330 $aThis book presents a wide range of optimization methods and their applications to various electrical power system problems such as economical load dispatch, demand supply management in microgrids, levelized energy pricing, load frequency control and congestion management, and reactive power management in radial distribution systems. Problems related to electrical power systems are often highly complex due to the massive dimensions, nonlinearity, non-convexity and discontinuity associated with objective functions. These systems also have a large number of equality and inequality constraints, which give rise to optimization problems that are difficult to solve using classical numerical methods. In this regard, nature inspired optimization algorithms offer an effective alternative, due to their ease of use, population-based parallel search mechanism, non-dependence on the nature of the problem, and ability to accommodate non-differentiable, non-convex problems. The analytical model of nature inspired techniques mimics the natural behaviors and intelligence of life forms. These techniques are mainly based on evolution, swarm intelligence, ecology, human intelligence and physical science. . 410 0$aAlgorithms for Intelligent Systems,$x2524-7565 606 $aElectrical engineering 606 $aEnergy systems 606 $aMathematics 606 $aMathematical optimization 606 $aElectrical Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T24000 606 $aEnergy Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/115000 606 $aMathematics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/M00009 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 615 0$aElectrical engineering. 615 0$aEnergy systems. 615 0$aMathematics. 615 0$aMathematical optimization. 615 14$aElectrical Engineering. 615 24$aEnergy Systems. 615 24$aMathematics, general. 615 24$aOptimization. 676 $a571.0284 702 $aPandit$b Manjaree$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDubey$b Hari Mohan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBansal$b Jagdish Chand$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910392750103321 996 $aNature Inspired Optimization for Electrical Power System$92539809 997 $aUNINA