外文翻译-应用识别和人工神经网络在电力系统负荷预测

英文部分ApplicationofPatternRecognitionandArtificialNeuralNetworktoLoadForecastinginElectricPowerSystemABSTRACTElectricpowersystemloadforecastingplaysanimportantroleintheEnergyManagementSystem(EMS),whichhasgreatinfluenceontheoperation,controllingandplanningofelectricpowersystem.Apreciseelectricpowersystemshorttermloadforecastingwillresultineconomiccostsavingandimprovingsecurityoperationcondition.Withthedevelop-mentofderegulationinelectricpowersystem,themethodofshorttermloadforecastingwithhighaccuracyisbecomingmoreandmoreimportant.Duetothecomplicacyanduncertaintyofloadforecasting,electricpowerloadisdifficulttobeforecastedpreciselyifnoanalysismodelandnumericalvaluealgorithmmodelisapplied.Inordertoimprovetheprecisionofelectricpowersystemshorttermloadforecasting,anewloadforecastingmodelisputforewordinthispaper.Thispaperpresentsashort-termloadforecastingmethodusingpatternrecognitionwhichobtainsinputsetsbelongtomulti-layeredfed-forwardneuralnetwork,andartificialneuralnetworkinwhichBPlearningalgorithmisusedtotrainsamples.Loadforecastinghasbecomeoneofthemajorareasofresearchinelectricalengineeringinrecentyears.Theartificialneuralnetworkusedinshort-timeloadforecastingcangraspinteriorruleinfactorsandcompletecomplexmathematicmapping.Therefore,itisworldwideappliedeffectivelyforpowersystemshort-termloadforecasting.Indexterms：artificialneuralnetwork(ANN)，backpropagation(BP)，learningalgorithm，loadforecast，patternrecognition.1.INTRODUCTIONShort-termloadforecastinghasbeenusefulinsafeandeconomicalplanningoperationofanelectricalpowersystem.Ithasbeenalsousedinstart-upandshut-downschedulesofgeneratingunits,overhaulplanningandloadmanagement.Oneofthecharacteristicsofelectricpoweristhatitcantbestockpiled,thatis,thepowerenergyisgenerated,transmitted,distributedandconsumedatthesametime1.Innormalworkingcondition,systemgeneratingcapacityshouldmeetloadrequirementanytime.Ifthesystemgeneratingcapacityisnotenough,essentialmeasureshouldbetakensuchasaddinggeneratingunitsorimportingsomepowerfromtheneighboringnetwork.Ontheotherhand,ifthesystemgeneratingcapacityisofsurplus,essentialmeasureshouldbetakentoo,suchasshutting-downsomegeneratingunits,oroutputtingsomepowertoneighboringnetwork.Loadvariationtrendandfeatureforecastingareessentialforpowerdispatch,layoutanddesigndepartmentofpowersystem.Havingbeentriedoutforlongtime,theloadforecastingmethodscanbesortedtoexperientialqualitativeforecastingandquantitativeone2.Experientialqualitativeforecastingmethodmostlydependsonthejudgmentfromsomeexperts.Itcanonlymakeadirectionalidea.ArtificialNeuralNetworkandExpertSystemmethodsbelongtoquantitativeforecastingmethods3-4.Artificialneuralnetwork,astheprototypeofhumanbraincells,canimitatethehumanbraintotrainknowninformation,graspinteriorruleinfactorsandcompletecomplexmathematicmapping5.Asisthecasewithtimeseriesapproach,theANNtracespreviousloadpatternsandpredictsaloadpatternusingrecentloaddata.Italsocanuseweatherinformationformodeling.TheANNisabletoperformnon-linearmodelingandadaptation.Itdoesnotneedassumptionofanyfunctionalrelationshipbetweenloadandweathervariablesinadvance6-8.WecanadapttheANNbyexposingittonewdata.Theirabilitytooutperformexperientialqualitativeforecastingmethodsespeciallyduringrapidlychangingweatherconditionsandtheshorttimerequiredtotheirdevelopment,havemadeANNbasedloadforecastingmodelsveryattractiveforonlineimplementationinenergycontrolcenters.Therefore,itisworldwideappliedeffectivelyforthepowersystemshort-termloadforecasting.Fig.1.Short-termloadforecastsystem2.SHORT-TERMLOAD-FORECAST(STLF)SYSTEMMODELTheshort-termloadforecastsystemisshowninFig.1.A.LoadDataGatheringThehistoricalloaddataandrealtimeloaddataareobtainedfromscandatasystembycomputer.Straightmethod,medianmethodandgreymodelareusedtogetridofbaddata.B.InputSetChoosingUsingpatternrecognitiontheory,thedatathattheirvaluesarehighlysimilartothatofpredictingdataarechosenasparametersandAnnsinputsets.Theparameterscanbeinfluencedbysomekindoffactors,suchastemperature,humidity,rainfallanddaytypesetc.Thustheyshouldbemappedwithinrange0，1,accordingtotheinfluencingextenttoload.Andthenthemappingdatabaseforloadsetsissetup,whichisthefoundationofchoosingsimilarday.C.LoadForecastThethree-layeredfeed-forwardartificialneuralnetworkwhichhasthefeatureofmemoryandlearningisconstructedandBackPropagationlearningalgorithmisusedtoobtainloadforecastingvalue.Theloadparametersettingischosentosetloadforecastingerrorlimitandthelike.D.LoadStatThemaximalload,minimalload,averageloadinonedaycanbeaddedupinthispart.E.ResultandFigureOutputEachkindofloadprofileanddataformcanbeshownandprintedinthispart.3.CHOOSINGLOADSETSUSINGPATTERNRECOGNITIONTHEORYOneoftheapproachestoimproveforecastingprecisionistoconsiderwhattheloadforecastingwouldbeinfluencedby,suchasweather,datetypesandespecialthingsetc.TheloadSetsarechosenusingoneofthebasictheoryofpatternrecognition,thatisclusteringanalysis.Itisnecessarytosetupaunifiedmappingdatabase.Thefactorsbywhichshort-termloadforecastingwouldbeinfluencedcommonlycontain:A.TheDayTypes(Workingday,thatisMondaytoFriday,ortherestday,thatisWeekendandallotherkindofholidays.)B.TheWeatherFactor(E.g.maximaltemperatureandminimaltemperatureofoneday,weatherstatus.etc.)C.TheEspecialThings(E.g.largepoliticalorculturalactivities)Thecharacteristicvariablesfortheneuralnetworkwillhavedifferentrangesiftheactualdataisdirectlyused.Therefore,thevariablesshouldbenormalizedsuchthattheywouldbewithintherange0，1.Iftheinfluenceonloadforecastingisgreater,therelevantmappingvalueshouldbegreater.4.BACKPROPAGATUIONLEARNINGALGORITHMBPneuralnetworktopologicalstructureisshowninFig.2,inwhichijVistheweightbetweeninputlayersandhiddenlayersandjkWistheweightbetweenhiddenlayersandoutputlayers.jisthebiasofhiddenlayersandisthebiasofoutputlayers.Back-propagationwascreatedbygeneralizingtheWidrow-Hofflearningruletomultiple-layernetworksandnonlineardifferentiabletransferfunctions.Afterpropagatedtohiddenlayers,agroupofinputsetsareactivatedbymodalfunctionorhyperbolictangentfunction,andthenthevaluewillbepropagatedtooutputlayers.Thevaluetransferredfromoutputlayerswillbecomparedwithexpectationsample.Ifithasbias,thevaluewillpropagatebackfromoutputlayers,andtheerrorreturnalongtheoriginalrouteway9.Atthesametime,weightandbiaswillbeadjustedtominimizeerror.Thedetailedoperationstepisshownasthefollowing.Fig.2BPNeuralnetworktopologicalstructure5.RESULTThereistrade-offinamountofdatathatcanbeinputtoANN.Themoredatainputsandthemoretrainingpatternstothenetworkthelongerthetrainingtimewhichdependingonthehardwareavailablecantakeseveralhours.Ontheotherhand,ifnotenoughdataandtrainingpatternsarepresentedtothenetworkthentheoutputwillnotberepresentativetothetrueinput.Thereisalsothepossibilityofconfusingthenetworkbyshowingitdatainwhichtherelationshipsareveryweakorevenmisguiding.Theproposedarchitectureistrainedbyusingbackpropa-gationalgorithm10with15and25patternsusingMatlabSIMULINKNNToolbox.TheperformanceoftheproposedpatternrecognitionneuralnetworkbasedSTLFmodelwastestedusinghourlyloaddata.resultsofthe24-houraheadforecastforJiangxiProvincepowersystemoveronedayperiodinsummerandwinterareshownasfollows.Fig.3andFig.4showthesummerforcastingresultwith15and25trainingpatternsrespectively.Fig.5.andFig.6showthewinterforcastingresultwith15and25trainingpatternsrespectively.Fromacomparisonwiththeexperientialqualitativeforecastingmethodamongthecurves,wecanseetheresultissatisfactory.6.CONCLUTIONShort-termloadforecastingisgivennewsignificanceaswellascomplexityduetotheemergenceofthenewcompetitiveelectricitymarketenvironment.Inthispaper,firstweintroducethesystemmodeloftheshort-termloadforecastingandexplaineverysectionparticularly.Thenwepresentsashort-termloadforecastingmethodusingpatternrecognitionwhichobtainsinputsetsbelongtomulti-layeredfed-forwardneuralnetwork,andartificialneuralnetworkinwhichBPlearningalgorithmisusedtotrainsamples.Atlastthecomputersimulationresultsindicatedthatthemethodcanforecastashort-termloadeffectively.AndTheproposedmethoddoesnotrequireheavycomputationaltimeandthatthepatternsconsideredfortrainingtheANNsalsohaveanimpactonforecastingaccuracy.中文部分应用识别和人工神经网络在电力系统负荷预测摘要电力系统负荷预测在能量管理系统(EMS)中发挥着重要作用，在控制和规划电力系统等操作上有很大的影响。一个精确的电力系统短期负荷预测将使经济成本得到节约并提高安全操作条件。随着放松管制的电力系统短期负荷预测的发展，高精度的预测方法正变得越来越重要。由于负荷预测的复杂性和不确定性，如果没有分析模型和数值算法模型，电力负荷难以精确预测。为了提高电力系统短期负荷预测的精度，一个新的负荷预测模型出现在本文的前言中。本文提出一种短期负荷预测方法使用模式识别，获得输入设置属于多层fed-forward神经网络和BP人工神经网络学习算法用于训练样本。负荷预测近年来已经成为一个电子工程的主要研究领域。人工神经网络应用于短期负荷预测可以掌握内部因素和完成复杂的数学映射规则。因此，它是世界广泛应用有效地对电力系统短期负荷预测。索引词:人工神经网络，反向传播，学习算法，负荷预测，模式识别1、介绍短期负荷预测在一个电力系统的安全经济计划运行是有用的。它也被用于启动和关闭安排机组的检修计划和负荷管理。电力的特点之一是，它不能储存，也就是说，电力能源生成、传播、分发和使用在同一时间1。在正常工作条件下，系统发电能力应该随时满足负载的要求。如果系统发电能力是不够的，应该采取诸如添加机组或从邻近的网络引进一些电力。另一方面，如果系统发电量有盈余，应采取必要的措施，如减少一些机组或向邻近的网络输出一些电力。在电力系统的布局和设计部门，负荷变化趋势和特征预测对于电力调度至关重要。已经尝试了很长时间，负荷预测方法可以分为经验定性预测和定量预测2。经验定性预测方法主要取决于一些专家的判断。它只能够是一个方向的想法。人工神经网络和专家系统方法属于定量预测方法3-4。人工神经网络，人类大脑细胞的原型，可以模仿人类大脑训练已知信息，掌握内部统治因素和完整的复杂数学映射5。时间序列方法一样，人工神经网络先前加载模式和预测负载模式痕迹使用最近的负载数据。它还可以使用天气信息建模。人工神经网络能够执行非线性建模和适应。它不需要假设任何功能提前加载和气象变量之间的关系(6-8)。我们可以让人工神经网络适应新的数据。能力优于经验定性预测方法特别是在快速变化的天气条件和发展所需的短时间内，让人工神经网络负荷预测模型非常有吸引力的基于线实现能源控制中心。因此，它是世界范围内应用有效的电力系统短期负荷预测。图1短期负荷预测系统2短期负荷预测系统模型短期负荷预测系统是图1所示。A：加载数据收集历史负荷数据和实时负荷数据直接由计算机从扫描数据系统中获取。直接法，中值法和灰色模型用于摆脱糟糕的数据。B：输入集选择利用模式识别理论，他们的数据值是高度相似的预测数据作为参数选择和神经网络的输入集。参数可以受到某种因素的影响，如温度、湿度、降雨量、日期类型等。因此他们应该映射到范围0，1内，根据影响程度上加载，然后映射数据库负载设置设置，选择相似日期的基础。C：负荷预测三层反馈人工神经网络的记忆和学习的特点是构造和反向传播学习算法的基础，主要用于获得负荷预测价值。负载参数设置是选择设置负荷预测误差范围等。D：负载状态一天内的最大负荷、最小负荷，平均负载都可以添加到这部分。E：结果和图输出各种负载概要文件和数据形式可以显示和打印在这部分。3利用模式识别理论选择负载集合提高预测精度的方法之一是考虑负荷预测的影响，比如天气