电力系统无功电压优化的模型与算法研究

华中科技大学硕士学位论文电力系统无功电压优化的模型与算法研究姓名：胡德峰申请学位级别：硕士专业：电力系统及其自动化指导教师：张步涵20060430IBPIIAbstractDuringtheconnectionofthebiggridsandtheapplicationofopen-accessmarketprinciples,thesecurityandstabilityofthepowersystemsbecomeabigchallenge.Theschemeofoptimalcoordinated-correlativevoltageandreactivepowercontrolarecanneatlyrealizeoptimalcoordinatecontrol,itscoresarereactivepoweroptimizationandvoltagestabilityanalysis.Themodelofreactivepoweroptimizationconcerningvoltagestabilitymarginispresentedbasedthetheoryofthebifurcation.Theequationalrestrictionsarepowerflowequationswithloadcoefficients,thepowerofallthenodesincreasesynchronously.Thedifferenceofloadcoefficientsbetweeninitialpowersystemandup-limitingstableoneafterreactivepoweroptimizationmeansvoltagestabilitymargin,makethepowersystemachievethreeaims:minimalpowerloss,bestvoltagequalityandmaximalvoltagestabilitymargin.Geneticalgorithmandparticleswarmoptimizationarethetwopopularalgorithmsthatareappliedinreactivepoweroptimization.Inthepaper,theoperationssuchascode,cross,mutationandpreservingbestindividualareimproved.WithBPartificialneuralnetwork,theresultofgeneticalgorithmbecomemoreprecise.TheParetooptimalsetisintroducedtoparticleswarmoptimizationformulti-objectivereactivepoweroptimization,themethodsofselectionofbestsituationsandfitnessfunctiondesignbasedParetooptimalsetareimproved.ThroughtheJacobimatrixeigen-valuestructureanalysisofstaticstablecriticalpoint,voltageunsubstantialregionandpivotalgeneratorsarefound,itgivesreferencetopreventivecontrolofvoltagestability.Keywords:reactivepoweroptimizationvoltagestabilitymargingeneticalgorithmparticleswarmsoptimizationbifurcationJacobimatrixeigen-valuestructureanalysis□______□“√”111.1:[1][2]([3][4])/“”[58]−[9][10][11][12]2[13][14](CSVC),[15][16][17]FACTSSCADA()[1820]−3[21][22][23][24]1-11-11dEMS/SCADA4EMS/SCADA/(EMS)/1.21.2.1(1)(2)(3)()(),[25]5(1)(2)1:min(,)(,)0(,)0fuxstguxhux=≤(1-1)(1-1)uxuxPVPV(1)221(,)minmin(2cos)LijijijijijNfgUUUUθ∈=+−∑(1-2)LNijgij−,ijUU,ijijθ,ij(2)21minminspecnjjspecjjUUfU=−=∆∑(1-3)nPQspecjUspecjU∆(3)Cmin[()()]()GCgptGigqtGicjcjiNjNCCPCQCQ∈∈=++∑∑(1-4)6GNCN()gptGiCPi()gqtGiCQi()cjcjCQj[26][27][28][29]PVPQPV(1-5)11(cossin)0(sincos)0niijijijijijjniijijijijijjPUUGBQUVGBθθθθ==−+=−−=∑∑(1-5)minmaxminmaxminmaxminmaxminmaxiiiGiGiGiCiCiCiiiiiiiUUUQQQQQQTTTIII⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅≤≤≤≤≤≤≤≤≤≤(1-6)iPiQi7iUjU,ijnijGij−ijBij−ijδ,ijmaxGiQ⋅minGiQ⋅imaxCiQ⋅minCiQ⋅imaxiU⋅miniU⋅imaxiI⋅miniI⋅imaxiT⋅miniT⋅i260(1).1981Karmarkar:[30][31][32][30]-8-[33][34][35](2):H.W.Domme1W.F.Tinney1968DavidTSunSQP[36]Kuhn-Tucker9(3)Benders[37](4)[38](ANN)ANNANN:;;10[39],[4042]−1.2.2(Difference-Differential-AlgebraicEquations,DDAE)()DDAEDDAE11(1)[43][44],[45](2)[46]2[47]12[48][49]()[21]1.3:233BPBP134561422.1:[50][52]−2.22.2.1(ODE)(,,)xfxpλ=(2-1)nxR∈lRλ∈15λkpR∈(,)0xfxλ==(2-2)λ(,)xfxλ=(2-3)λ(2-2)(2-3)[53](DAE):(,,,)(,,)(,,,)0xfxypxFzpgxypλλλ==(2-4)nxR∈lRλ∈kpR∈0000,,,xypλ000000000(,,,)0(,,,)fxypgxypλλ==(2-5)(,,,)yDgxypλ1(,,)yyxpλ−=(2-6)1(,(,,),,)xfxyxppλλ−=(2-7)[54][55]16(saddle-nodebifurcation)(limit-inducedbifurcation[56])2.2.2(0λλ=):1)(,)0fxλ=2)(,)0fxλ=3)0TxxDfvDfw∗∗==(2-8)*0TwDfλ≠(2-9)2*0TxwDfvv≠(2-10),nwvR∈(,)0fxλ=2.2.3(PV)(PQ),;2-1172-12-1(a)2-1(b)UtimePUUtimePU2-12.318PVPV.Newton-Raphson2.3.1(,)001TxTfxDfyyyλ∗===(2-11)[57]2.3.2[58][60]−2-2(A)(B)(C)(D)(E)19ABCDEFG2-22.3.3[61]:;min-λ(2-12)..(cossin)0GiLiijijijijijpiBistPPUUGBbiSδδλ−−+−=∈∑(2-13)20(sincos)0GiLiijijijijijQiBiQQUUGBbiSδδλ−−+−=∈∑(2-14)iiiGGGGPPPiS∈(2-15)iiiRRRRQQQiS∈(2-16)iiiBVVViS∈(2-17)iiiTKKKiS∈(2-18)iiiCCCCBBbiS∈(2-19)λbBSGSRSKTSCB/CS/(2-13)(2-14)λPbQb1,,,,TPPPnbbb=1,,,,TQQQnbbb=PQbbλPV(2-12)-(2-19)2.3.421minλ−(2-20)..()0stfxbλ+=(2-21),xbnfn(,,)(())TFxyyfxbλλλ=−++(2-22)()0Fyfxbλ∂∂=+=(2-23)()0TFxfxy∂∂=∇=(2-24)10TFybλ∂∂=−+=(2-25)(2-23)(2-24)(2-25)(2-25)0y≠0y∃≠()0Tfxy∇=()Tfx∇n()Tfx∇[62][63]P-V()222.42.4.1(2-26)00(,)xλ(,)xλ0min()λλ−−(2-26)0,0:(,)0(,,)0stFxuFxuλλ==(2-27)000xxxxxxuuu(2-28)2.4.21(1)minlossP(2-29)23(2)2max1minspecniiiiVVVV=−∆=∆∑(2-30)niVispeciVimaxmin2speciiiVVV+=maxiV∆maxmaxminiiiVVV∆=−(3)0min()λλ−−(2-31)2(1)00(,,)0(,,)0FxuFxuλλ==(2-32)0xxu0λλ(2):;GiGiGiGPPPiS∈24RiRiRiRQQQiS∈iiiBVVViS∈(2-33)minmin1max1max[,,...,,]TTTTTTKKKKKiS+−∈∈minmin1max1max[,,...,,]iCCCCCCQQQQQS+−∈∈BSGSRSTKTSCQ\CS\3u:12,1212,,,,,,,,,gctGGGnCCCnTTTnuVVVBBBKKK=⋅⋅⋅⋅⋅⋅⋅⋅⋅4/(1)GiVGiV(2)CiBiiYCiY'CiYiCiY'CiYi''iiiiCiCiYYYY=+−(2-34)(3)TiK25ij'1Zij:1k'2Z'1Zij'2Z21Tkyk−1Tkyk−TykTZ2-3,ij2''1211,,,TTTijjiiijjTyyyYYYYykkkZZ===+=+(2-35)k'k,ij'22'1111(),(),0iiTijTjjYyYyYkkkk∆=−∆=−∆=(2-36)2.5263BP3.1(GeneticAlgorithm)3[64](1)(2)minmax[,]UU27(1)(2)[41]Na.GRCtNNNN+++[0,1]maxminmin()iiiiiXrXXX⋅⋅⋅=−+ir[0,1]GNRNCNtNiXmaxiX⋅miniX⋅XXb.Xc.XaNN283.2[6569]−(1)[65]__minmax()iixµµ−1,2,im=⋅⋅⋅()ixµ__iµ1min1()()losslossPxPxµ=(3-1)minlossP()lossPxmin2()()VxVxµ∆=∆(3-2)minV∆()Vx∆(3-1)(3-2)1()xµ2()xµ≤1293min()()xxλµλ∆=∆(3-3)minλ∆()xλ∆3()1xµ≤(2)0.60.6(3)UCmaxtUUUCCα=maxmaxUUUUCCCC≥(3-4)UClossPlossPUClossPQCmaxtQQQCCα=maxmaxQQQQCCCC≥(3-5)1.032Uα