畸變電網(wǎng)下PWM整流器魯棒預(yù)測控制研究

畸變電網(wǎng)下PWM整流器魯棒預(yù)測控制研究摘要：畸變電網(wǎng)在現(xiàn)代工業(yè)生產(chǎn)中廣泛存在，其復(fù)雜性和不穩(wěn)定性對電力系統(tǒng)的穩(wěn)定運(yùn)行產(chǎn)生了深遠(yuǎn)影響。針對畸變電網(wǎng)下PWM整流器的魯棒控制問題，本文提出了一種基于預(yù)測控制的解決方案。首先，通過建立畸變電網(wǎng)下PWM整流器的動態(tài)數(shù)學(xué)模型，利用基于神經(jīng)網(wǎng)絡(luò)的系統(tǒng)辨識技術(shù)進(jìn)行參數(shù)辨識，建立了一個能夠準(zhǔn)確反映畸變電網(wǎng)特性的系統(tǒng)模型。然后，采用基于RBF神經(jīng)網(wǎng)絡(luò)的預(yù)測控制算法進(jìn)行預(yù)測和控制，利用控制器對PWM整流器進(jìn)行魯棒性調(diào)節(jié)，實(shí)現(xiàn)了對畸變電網(wǎng)下PWM整流器的魯棒控制。最后，通過仿真實(shí)驗(yàn)驗(yàn)證了該預(yù)測控制算法的可行性和有效性。

關(guān)鍵詞：畸變電網(wǎng)；PWM整流器；魯棒控制；預(yù)測控制；RBF神經(jīng)網(wǎng)絡(luò)

Abstract:Distortedpowergridhasbeenwidelyexistinmodernindustrialproduction,anditscomplexityandinstabilityhaveprofoundimpactonthestableoperationofpowersystem.InordertosolvetherobustcontrolproblemofPWMrectifierunderdistortedpowergrid,thispaperproposesasolutionbasedonpredictivecontrol.Firstly,byestablishingthedynamicmathematicalmodelofPWMrectifierunderdistortedpowergrid,usingthesystemidentificationtechnologybasedonneuralnetworkforparameteridentification,weestablishedasystemmodelthatcanaccuratelyreflectthecharacteristicsofdistortedpowergrid.Then,thepredictivecontrolalgorithmbasedonRBFneuralnetworkisusedforpredictionandcontrol,andthecontrollerisusedtoadjusttherobustnessofPWMrectifier,realizingtherobustcontrolofPWMrectifierunderdistortedpowergrid.Finally,thefeasibilityandeffectivenessofthepredictivecontrolalgorithmareverifiedbysimulationexperiments.

Keywords:Distortedpowergrid;PWMrectifier;Robustcontrol;Predictivecontrol;RBFneuralnetworInrecentyears,theuseofpowerelectronics-basedsystemssuchasPWMrectifiershasincreasedrapidlyduetotheirhighefficiencyandexcellentperformance.However,theoperationofsuchsystemsinadistortedpowergridcancausesignificantchallenges.Thedistortioninthepowergridcanresultinseveralissuessuchasreducedpowerquality,decreasedsystemefficiency,andeveninstability.Therefore,therobustcontrolofPWMrectifiersunderdistortedpowergridconditionshasbecomeanimportantresearchtopic.

Toaddressthischallenge,apredictivecontrolalgorithmbasedonRBFneuralnetworkisproposedinthisstudy.ThealgorithmutilizestheRBFneuralnetworktopredicttheoutputvoltageandcurrentofthePWMrectifierunderdifferentoperatingconditions.ThepredictedvaluesarethenusedbythecontrollertoadjusttherobustnessofthePWMrectifier.ThecontrolobjectiveistomaintainthedesiredoutputvoltageandcurrentofthePWMrectifierunderdistortedpowergridconditions.

Theproposedalgorithmwastestedthroughsimulationexperiments.TheresultsshowedthatthealgorithmwasabletoeffectivelymaintainthedesiredoutputvoltageandcurrentofthePWMrectifierunderdistortedpowergridconditions.ThesimulationsalsoshowedthattheproposedalgorithmhadbetterperformancecomparedtotraditionalPIcontrollers.

Inconclusion,theproposedpredictivecontrolalgorithmbasedonRBFneuralnetworkisaneffectivewaytoachieverobustcontrolofPWMrectifiersunderdistortedpowergridconditions.ThealgorithmcanimprovetheperformanceandstabilityofPWMrectifiers,henceimprovingpowerqualityandefficiency.FurtherresearchcanbeconductedtooptimizethealgorithmforpracticalapplicationsInadditiontotheproposedalgorithmbasedonRBFneuralnetwork,thereareotheradvancedcontrolstrategiesthatcanbeusedforPWMrectifiers.Onesuchstrategyisthemodelpredictivecontrol(MPC)whichisgainingincreasedattentioninrecentyearsduetoitsabilitytohandlecomplexcontrolproblems.MPCisapredictivecontrolmethodthatusesamathematicalmodelofthesystemtopredictthesystem'sfuturebehaviorandoptimizeacostfunctionoverafinitehorizon.TheadvantageofMPCovertraditionalcontroltechniquesisthatitcanhandleconstraintsanduncertainties,makingitasuitablechoiceforpowerelectronicssystems.

AnothercontrolstrategythatcanbeusedforPWMrectifiersisadaptivecontrol.Adaptivecontrolisatypeofcontrolthatadjuststhecontrollerparametersbasedonthechangesinthesystem'sdynamics.Thismeansthatthecontrollercanadapttovaryingoperatingconditions,makingitmoreflexibleandrobust.However,adaptivecontrolrequiresathoroughunderstandingofthesystem,andthedesignofthecontrollercanbemorechallengingcomparedtotraditionalcontrolmethods.

Moreover,theapplicationofartificialintelligence()techniquessuchasfuzzylogic,geneticalgorithms,andreinforcementlearning,hasshownpromisingresultsinthecontrolofpowerelectronicssystems.Forinstance,thefuzzylogiccontroller(FLC)isanon-linearcontroltechniquethatcanhandleuncertaintiesandnon-linearitiesinthesystem.FLCcanbeusedtodevelopacost-effectivecontrolstrategyforPWMrectifiersthatcanachievegoodperformanceunderdistortedpowergridconditions.

Inconclusion,thecontrolofPWMrectifiersisachallengingtaskduetothenon-linearandcomplexnatureofthesystem,andthepresenceofdistortedpowergridconditions.However,advancedcontrolstrategiessuchasMPC,adaptivecontrol,and-basedtechniquesofferapromisingapproachforachievingrobustandefficientcontrolofPWMrectifiers.FutureresearchcanfocusonthedevelopmentandimplementationoftheseadvancedcontrolstrategiesforpracticalapplicationsOneareaofresearchforfuturedevelopmentinPWMrectifiersistheintegrationwithrenewableenergysources,suchaswindandsolarpower.Thefluctuatingnatureofrenewableenergysourcescreateschallengesforstableandefficientoperationofthepowergrid.PWMrectifierscanplayaroleinbalancingthepowersupplyanddemand,andadvancedcontrolstrategiescanbedevelopedtooptimizetheperformanceofthepowergrid.

AnotherareaofresearchistheapplicationofPWMrectifiersinelectricvehicles.Withtheincreasingpopularityofelectricvehicles,thedemandforefficientandreliablepowerconvertersisgrowing.PWMrectifierscanbeusedasbatterychargersandmotordrivesinelectricvehicles.Advancedcontrolstrategiescanbeemployedtoensuresafeandfastcharging,andhigh-performancemotorcontrol.

Moreover,thedevelopmentofhardware-in-the-loop(HIL)simulationplatformscanfacilitatethetestingandvalidationofadvancedcontrolstrategiesforPWMrectifiers.HILsimulationallowsthecontrolalgorithmstobetestedinarealisticenvironment,withouttheneedforexpensiveandtime-consuminghardwaretesting.HILsimulationcanacceleratethedevelopmentanddeploymentofadvancedcontrolstrategiesforPWMrectifiers,andhelptoimprovetheefficiencyandreliabilityofpowerelectronicssystems.

Finally,theintegrationofartificialintelligence()techniques,suchasdeeplearningandreinforcementlearning,canfurtherenhancetheperformanceofPWMrectifiers.techniquescanlearnfromthesystembehaviorandadaptthecontrolstrategiesinreal-time,leadingtoimprovedefficiency,robustness,andreliability.However,thedevelopmentof-basedcontrolalgorithmsrequireslargeamountsoftrainingdataandcomputationalpower,andcarefulconsiderationofsafetyandethicalconcerns.

Insummary,thecontrolofPWMrectifiersisachallengingtask,butadvancedcontrolstrategiesandresearchareassuchasrenewableenergyintegration,electricvehicles,