Solving OPF Problems using Biogeography Based and Grey Wolf Optimization Techniques

Int. J. Energy Optim. Eng. Pub Date : 2017-07-01 DOI:10.4018/IJEOE.2017070103

Kingsuk Majumdar, Puja Das, P. Roy, Subrata Banerjee

引用次数: 5

Abstract

Thispaperpresentsbiogeography-basedoptimization(BBO)andgreywolfOptimization(GWO)for solvingthemulti-constrainedoptimalpowerflow(OPF)problemsinthepowersystem.Inthispaper, theproposedalgorithmshavebeentestedin9-bussystemundervariousconditionsalongwithIEEE 30bustestsystem.Acomparisonofsimulationresultsrevealsoptimizationefficacyoftheproposed schemeoverevolutionaryprogramming(EP),geneticalgorithm(GA),mixed-integerparticleswarm optimization(MIPSO)fortheglobaloptimizationofmulti-constraintOPFproblems.Itisobserved thatGWOisfarbetterincomparisontootherlistedoptimizationtechniquesandcanbeusedfor aforesaidproblemswithhighefficiency. KEyWORdS Biogeography Based Optimization, Grey Wolf Optimization, Migration, Mutation, Optimal Power Flow

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利用生物地理学和灰狼优化技术求解OPF问题

Thispaperpresentsbiogeography-basedoptimization(BBO)andgreywolfOptimization(GWO)for solvingthemulti-constrainedoptimalpowerflow(OPF)problemsinthepowersystem。Inthispaper, theproposedalgorithmshavebeentestedin9-bussystemundervariousconditionsalongwithIEEE 30bustestsystem。Acomparisonofsimulationresultsrevealsoptimizationefficacyoftheproposed schemeoverevolutionaryprogramming(EP)，geneticalgorithm(GA)，mixed-integerparticleswarm optimation_ (MIPSO)fortheglobaloptimizationofmulti-constraintOPFproblems。Itisobserved thatGWOisfarbetterincomparisontootherlistedoptimizationtechniquesandcanbeusedfor aforesaidproblemswithhighefficiency。关键词:基于生物地理学的优化，灰狼优化，迁移，突变，最优潮流

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Int. J. Energy Optim. Eng.

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