基于短期负荷预测技术的电能控制系统研究

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1、天津大学博士学位论文基于短期负荷预测技术的电能控制系统研究姓名：王硕禾申请学位级别：博士专业：电力电子与电力传动指导教师：万健如20081201ABSTRACTConsideringthesituationthatelectricalcostofindustryathomeandabroadincludestwoparts(basiccostandactualcost)，anenergy—savingelectricity-consumption-controlsystemisproposedformetallurgicalenterpriseswithmanyhigh-powe

2、relectricarcfurnaces，whichconsulneagreatdealofenergy，toreducetheirbasicelectricalcostbyevenlyregulatingthepoweranddecreasingthemaximumload．Efficientshort-termloadforecastingalgorithmisthecoreissueofthisstudy．Followingachievementshavebeenobtained：Detailedanalysistocurrentsimilartechnologyathom

3、eandabroadiscarriedout．皿1emainshort-termloadforecastingalgorithmsandtheirprinciples，methodsandcharacteristicsarediscussedindepth．Actualbackground，necessityandsignificanceofthisstudyareexplained．Consideringthedifficultiesofselectingtheelectricpowercapacityofhigh-powerelectricalefurnacesresulte

4、dbydrasticpowerfluctuation,anewarithmeticbasedonthethresholdtheoryispresentedoriginally。乃eenergyfunctionofthresholdvalueisconstructedbythemethodofvarianceanalysis，andthebaseforselectingthresholdvalueiSobtained。Theformulasforcalculatingthecrossingintensityarededucted．nlerationali够oftheselected

5、thresholdisverifiedbythecrossingintensityoftheinstantpowerofpower-supplysystemtothethreshold．Basedonhistoricalloaddata,themethodisputforwardfordeterminingtheoptimaldatalengthofthegreymodelGM(1，1)，andtheforecastingresultsareamendedbynovelmethodssuchasresidualrevisionandfillinginnovationinprope

6、rorder．neG-G-NNalgorithmisproposedbycombiningthecharacteristicsofgreytheory，reconstruction—phase-spaceGPalgorithmandartificialneuralnetworkfrCN)．UsinggreytheoryandG．Palgorithm,thisnewalgorithmconverttheoriginaltimeseriesintothetimeseriesphasespacewithstrongorderliness，andthentheloadispredicte

7、dbyNN．Thepredictedresultsareofhigherprecisionandbetterreal—timethanneuralnetwork．Aimingatthefluctuationandperiodicityoftheloadseries，thenewmethodbasedonthewaveletexcellentcharacteristicstoanalysistimeandfrequencyiSsuggested，withwhichthesignal