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机组最优化问题的改进遗传算法
Abstract
Theoptimaloperationandcontrolofpowergenerationunitsiscriticalinensuringefficientandreliablepowergeneration.Animprovedgeneticalgorithmisproposedforsolvingtheunitoptimizationproblem.Theproposedalgorithmisdesignedtoimprovetheconvergencespeedandsolutionaccuracyofthetraditionalgeneticalgorithm.Thealgorithmisevaluatedusingtestcases,anditsresultsarecomparedwithotheroptimizationalgorithms.Theresultsshowthattheproposedalgorithmisabletoprovidebettersolutionsmoreefficientlythanothermethods.
Introduction
Powergenerationunitsarevitalcomponentsofpowersystems.Theoptimaloperationandcontroloftheseunitsarecriticalinensuringefficientandreliablepowergeneration.Unitoptimizationinvolvesfindingtheoptimaloperatingpointoftheunit,whichminimizesthecostofgeneratingpowerwhilemeetingdemandandsystemconstraints.Unitoptimizationisacomplexandchallengingproblem,duetothenonlinearity,uncertainandnon-stationarityoftheunit,aswellasthepresenceofmultipleobjectivesandconstraints.
Existingoptimizationalgorithms,suchaslinearprogramming,non-linearprogramming,anddynamicprogramming,havebeenusedtosolvetheunitoptimizationproblem.However,thesemethodssufferfromhighcomputationcomplexityandlackofglobaloptimalsolutions.Geneticalgorithms(GA)haveemergedasapowerfultoolforsolvingthesetypesofcomplexoptimizationproblems.
Geneticalgorithmsareatypeofevolutionaryalgorithmthatmimicstheprocessofnaturalselection.Theyarebasedontheprinciplesofgeneticsandevolution,andaremodeledaftertheprocessofnaturalselection.GAshavebeensuccessfullyusedtosolveawiderangeofoptimizationproblems,duetotheirabilitytohandlenon-linear,non-convex,andmulti-objectiveproblems.
Inthispaper,animprovedgeneticalgorithmisproposedforsolvingtheunitoptimizationproblem.Theproposedalgorithmisdesignedtoimprovetheconvergencespeedandsolutionaccuracyofthetraditionalgeneticalgorithm.Thealgorithmisevaluatedusingtestcases,anditsresultsarecomparedwithotheroptimizationalgorithms.
TheProposedAlgorithm
Theproposedalgorithmisanimprovedgeneticalgorithmthatuses