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1、第44卷第504期電測與儀表Vol.44No.5042017年第12期ElectricalMeasurement&InstrumentationDec.2017基于改進FOA優(yōu)化BP神經(jīng)網(wǎng)絡(luò)算法的光伏系統(tǒng)MPPT研究*閆超1,2，倪福佳1，劉嘉瑜1,2，賀詩明2，高振遠2，王少帥1（1.中國礦業(yè)大學(xué)江蘇省煤礦電氣與自動化工程實驗室，江蘇徐州221116；2.中國礦業(yè)大學(xué)電氣與動力工程學(xué)院，江蘇徐州221116）摘要：針對基于BP神經(jīng)網(wǎng)絡(luò)的光伏系統(tǒng)MPPT策略在光照強度突變時存在較大誤差的問題，本文提出了一種改進的果蠅優(yōu)化算法用于BP神經(jīng)網(wǎng)絡(luò)的權(quán)值和
2、閾值優(yōu)化，并建立了基于IFOA-BP神經(jīng)網(wǎng)絡(luò)算法的光伏系統(tǒng)MPPT控制的仿真模型。測試和仿真結(jié)果表明，IFOA的收斂速度和求解精度較改進前均有明顯提升；IFOA優(yōu)化后的BP神經(jīng)網(wǎng)絡(luò)收斂速度加快，預(yù)測誤差減少；較之于電導(dǎo)增量法，IFOA-BP神經(jīng)網(wǎng)絡(luò)的MPPT策略在穩(wěn)態(tài)條件下能明顯抑制功率波動，在外界條件發(fā)生突變時，能迅速準確地追蹤到最大功率點，具有良好的穩(wěn)態(tài)精度和動態(tài)特性。關(guān)鍵詞：光伏電池；最大功率點跟蹤；BP神經(jīng)網(wǎng)絡(luò)；改進果蠅優(yōu)化算法中圖分類號：TM933文獻標識碼：B文章編號：1001-1390（2018）00-0000-00Researcho
3、fonthephotovoltaicsystemMPPTbasedonimprovedIFOA-BPneuralnetworkalgorithmYanChao1,2,NiFujia1,LiuJiayu1,2,HeShiming1,GaoZhenyuan1,WangShaoshuai1(1.JiangsuProvinceLaboratoryofElectricalandAutomationEngineeringforCoalMining,ChinaUniversityofMining&Technology,Xuzhou221116,Jiangsu,C
4、hina.2.SchoolofElectricalandPowerEngineering,ChinaUniversityofMining&Technology,Xuzhou221116,Jiangsu,China)Abstract：:WhentheBPneuralnetworkisadoptedtopredictthevoltageatthemaximumpowerpoint,thereisabigerrorifthelightintensitychangesdrastically.Aimingatthisproblem,anovelimprove
5、dfruitflyoptimizationalgorithm(IFOA)determiningtheoptimalBPneuralnetworkparameters(weightandthreshold)isproposed,andasimulationmodelofthephotovoltaicsystemMPPTcontrolstrategybasedontheIFOA-BPneuralnetworkalgorithmwasisestablished.Thetestandsimulationresultsshowthatthe,IFOAhasa
6、greatadvantageinconvergencesearchspeedandsolutionaccuracythanFOA;IFOA-BPneuralnetworkcaneffectivelyincreasestheconvergencespeedandreducesthepredictionerror;.comparedComparedwiththeincrementalconductance(INC)method,theproposedphotovoltaicsystemMPPTcontrolalgorithmbasedonIFOA-BP
7、neuralnetworkcouldsuppresstheoscillationaroundthemaximumpowerpoint(MPP)understeady-stateconditionsandtrackdowntheMPPquicklyandaccuratelywhenlightintensityandtemperaturechangedrastically,whichverifiesthestability,precisionandrapidityoftheproposedMPPTmethod.Keywords：:photovoltai
8、ccell,maximumpowerpointtracking,BPneuralnetwork,improvedfruit
