提升小波在電能質(zhì)量分析中的應(yīng)用研究.pdf

1、南京理工大學(xué)博士學(xué)位論文提升小波在電能質(zhì)量分析中的應(yīng)用研究姓名：楊漢生申請學(xué)位級別：博士專業(yè)：控制科學(xué)與工程指導(dǎo)教師：楊成梧20060526摘要博士學(xué)位論文ABSTRACTTheproliferationofpowerelectronicdevicesandnonlinearloadshastriggeredarowingconcernwithpowerquailtyTraditionalpowerqualityanalysisisba

2、sedonffectivevaluetheoryAsamainanalysistool，F(xiàn)ouriertransformisnotsuitablefortheonstationarysignalsprocessingSoitisindispensabletOestablisheffectivedetectionndanalysissystemtodetectandclassifydynamicpowerqualitydisturbanc

3、esUsingliftingwavelettransformtheoryandANNtechniquesthispaperpresentsasystematicstudyofpowerqualitydisturbancesdetectionandclassificationThemaincontentsofwhicharcasfoUOWS：1Itsummarizestheliftingwavelettransformtheorypoin

4、tsOUttheadvantageoftheliftingwavelettransformThenanovelpowerqualityanalysismethodbasedonliftingwavelettransformwithadaptiveperformanceisproposedandsuccessfullyappliedindynamicpowerqualityanalysisforthefifsttime2Becauseth

5、echaracteristicofthewavelettrailsformmodulusmaximarelatedtothepowerqualit)，disturbancesingularitypointsandthenoiseisdifcrent，soitisnotdifficulttodistinguisheachotherinthehJlghscalesBasedonthisphenomenaasoft—thresholddeno

6、isingmethodisintroduced，whichCanlocatethepowerqualitydisturbancestartpointandendpointinthenoisyconditionexactly3Throughanalyzingtherelationbetweenbuuerworthfiltersandorthonormal‘’‘’——’—。waveletsanewapproachforpowermeasur

7、mentisproposed，whichCanimprovetheaccuracyofmagnitudeofdisturbances4InordertoclassifyandidentifyvarioustypesofpowerqualitydisturbancesaclassifierbasedonliftingwaveletandneuralnetworkisusedFurthermore，somefeaturevectorsbas

8、ed0ntheliftingwavelettransformarededucedwhichCallkeepuniqueThenanartificialneuralnetworkintegratedwitllliftingwaveletisusedtOclassifyvarioustypesofpowerqualitydisturbancesKeywords：power刪ity；liftingmavelet自ransform；artifi