Multi-objective probabilistic distribution feeder reconfiguration considering wind power plants

•Investigating Wind Turbine (WT) effect on Multi-objective Distribution Feeder Reconfiguration (MDFR).•Considering the uncertainty of loads and WTs by Point Estimate Method through MDFR problem.•Proposing a novel self adaptive modification approach based on Teacher Learning Optimization (TLO).•Utili...

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Veröffentlicht in:	International journal of electrical power & energy systems Jg. 55; S. 680 - 691
Hauptverfasser:	Kavousi-Fard, Abdollah, Niknam, Taher, khosravi, Abbas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 01.02.2014 Elsevier
Schlagworte:	Algorithms Applied sciences Direct energy conversion and energy accumulation Electric power generation Electric power plants Electrical engineering. Electrical power engineering Electrical power engineering Energy Exact sciences and technology Feeders Mathematical models Miscellaneous Multi-objective Probabilistic Distribution Feeder Reconfiguration (MPDFR) Natural energy Non classical power plants Optimization Power networks and lines Probabilistic methods Probability theory Self Adaptive Modified Teacher Learning Optimization (SAMTLO) algorithm Uncertainty Wind energy Wind power plants Wind Turbines (WTs) Uncertainty Multi-objective Probabilistic Distribution Feeder Reconfiguration (MPDFR) Self Adaptive Modified Teacher Learning Optimization (SAMTLO) algorithm Wind Turbines (WTs) Performance evaluation Costs Self Adaptive Modified Teacher Learning Output power Demand forecasting Optimization method Multiobjective programming Wind generator Feeder Reconfiguration (MPDFR) Wind power plant Power losses Reconfigurable architectures Learning algorithm Distribution network Probabilistic approach Pareto optimum Reactive load Forecasting management Constrained optimization Case study Electrical network Optimal solution Optimization (SAMTLO) algorithm Load management Objective function Effectiveness factor Multi-objective Probabilistic Distribution
ISSN:	0142-0615, 1879-3517
Online-Zugang:	Volltext
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