1?GOLDWINDSCIENCE&TECHNOLOGYCO.,LTD.測風數(shù)據(jù)長期訂正對風資源評估的影響Theimpactoflong-termcorrectionofwindmeasurementdataonwindresourceassessment北京金風科創(chuàng)風電設(shè)備有限公司BEIJINGGOLDWINDSCIENCE&CREATIONWINDPOWEREQUIPMENTCO.,LTD風資源技術(shù)部WindResourceAssessmentDepartment高級風資源工程師：楊長鋒SeniorWindResourceEngineer：ChangfengYang長期訂正的作用及意義Thefunction

andsignificanceoflong-termrevision01算法匯總Algorithmsummary02結(jié)果驗證Results03結(jié)論及相關(guān)進展Conclusion&Relatedprogress04CONTENTS目錄3長期訂正的作用及意義Thefunctionandsignificanceoflong-termrevision數(shù)據(jù)清理參數(shù)計算風速風頻分布極端風速入流角湍流切變水平外推垂直外推降低LCOE計算的不確定度，為項目評估提供支持提高載荷輸入?yún)?shù)準確性算法匯總AlgorithmsummaryAlgorithmTypeAbbrev.DescriptionLinearLeastSquaresLLSTheclassicleastsquaresfittothescatterplotoftargetandreferencespeedsTotalLeastSquaresTLSAslightmodificationofLLSthatminimizesorthogonaldistancetothebestfitVerticalSliceVSApiecewiselinearfittotthescatterplotoftargetandreferencespeedsVarianceRatioVRAsimplelinearmappingthataimstopreservethevarianceofthetargetdataBulkSpeedRatioBSRThesimplestpossiblealgorithm,basedontheratioofmeanwindspeedsWeibullFitWBLApowerlawfitwhoseparametersderivefromtheWeibullparametersofthetargetandreferencedataSpeedSortSSAlinearfittotherelationshipbetweentargetandreferencecumulativefrequencydistributionsMatrixTimeSeriesMTSAnimplementationoftheclassicmatrixmethodthatwemodifiedtoproducerealistictimeseriesdata整體最小二乘TotalLeastSquaresAlgorithm線性最小二乘LinearLeastSquaresAlgorithm分風速段線性回歸VerticalSliceAlgorithm方差比值法VarianceRatioAlgorithm風頻擬合法WeibullFitAlgorithm速度追蹤法SpeedSortAlgorithm風速序列聯(lián)合概率分布MartixTimeSeriesAlgorithmCASE1CASE2CASE3CASE4Duration6years6years3years3yearsR^20.6440.5750.6210.598RecoveryRate99.29%99.93%99.69%99.54%Timestepsofcomparison60min60min60min60mintimes212199評價指標WSKWPDAEPSD√？☆★RMSE√？☆★計算結(jié)果及驗證思路Ideasforverifyingcalculationresults1month3month6month9month12month15monthBSR

0.2124

0.1319

0.0672

0.0442

0.0319

0.0292MTS

0.2067

0.1362

0.1105

0.0686

0.0243

0.0404SS

0.1965

0.1214

0.0590

0.0461

0.0330

0.0323TLS

0.2047

0.1239

0.0602

0.0496

0.0350

0.0332VR

0.2128

0.1311

0.0685

0.0502

0.0347

0.0340WBL

0.2265

0.1386

0.0716

0.0493

0.0342

0.0349LLS

0.1833

0.1073

0.0701

0.0463

0.0377

0.0317時間長度對計算結(jié)果的影響Theeffectoflengthofdataonthecalculationresults結(jié)果驗證-風速Result–WindSpeedMast1Mast2Mast3Mast4BSR0.05720.06050.03250.0406LLS0.04850.05610.02580.0361MTS0.05470.06510.03320.0471SS0.05460.06950.03340.0424TLS0.05960.06790.02910.0426VR0.05870.06960.02870.0430VS0.04450.05150.02500.0367WBL0.06110.06710.02750.0429New0.04590.05250.02390.0327結(jié)果驗證-K值Result–KvalueMast1Mast2Mast3Mast4BSR0.06900.21550.08990.0783LLS0.53200.58290.45980.4321MTS0.22140.20340.20350.1821SS0.11250.08470.11370.0681TLS0.12130.10240.11200.0980VR0.11350.07710.10590.0817VS0.62020.60130.49020.4373WBL0.10580.07830.10150.0741New0.06100.05790.06780.0383結(jié)果驗證-風功率密度Result–WindPowerDensityMast1Mast2Mast3Mast4BSR7.934317.397314.76118.3066LLS38.670835.319033.557631.0287MTS18.912116.255417.735715.2753SS8.50776.90768.38656.9202TLS8.48539.23768.98768.4063VR8.09767.36147.76037.1570VS42.350635.116835.485530.6848WBL8.15046.21838.55056.7247New7.56876.12577.04754.2817結(jié)果驗證-發(fā)電量Result–AnnualEnergyProductionMast1Mast2Mast3Mast4BSR152467.2145961.380031.43154478.9LLS105321.1372920.395588.4894103.6MTS126250.5245128.956274.3136752.6SS139641107285.556274.3136752.6TLS140613.8100627.778606.19143524.6VR139840.9108498.678719.25150406.5VS214058492175.366243.53188772.1WBL150526111711.556960.36150968.8New92524.47107033.546819.97104563.3季節(jié)因素對長期訂正的影響十分重要，隨測風時間增加，風速訂正不確定度會明顯降低。Theinfluenceofseasonalfactorsonlong-termcorrectionis

important.Asthewindmeasurementtimeincreases,thewindspeedcorrectionuncertaintywillbesignificantlyreduced.在進行長期訂正時，不僅要注意風速的變化值，同時要注意K值的變化幅度，避免造成風功率密度產(chǎn)生較大差異。Inthelong-termcorrection,toavoidalargedifferenceinthecalculationofthewindpowerdensity.PayattentiontothevariationoftheKvaluewhilepayingattentiontothechangevalueofthewindspeed.結(jié)論Conclusion線性回歸方法在風速訂正上與其他方法差異不大，但會較大幅度改變K值，對發(fā)電量計算產(chǎn)生較大影響。尤其是實測風速K值較小時，誤差會更加明顯。Thelinearregressionmethodhaslittledifferencewithothermethodsinwindspeedcorrection,butitwillchangetheKvaluegreatly,whichhasagreatimpactonthecalculationofpowergeneration.EspeciallywhenthemeasuredwindspeedKvalueissmall,theerrorwillbemoreobvious.優(yōu)化后的算法與其他MCP算法相比，在風速、K值和發(fā)電量計算上誤差較小且表現(xiàn)較為穩(wěn)定。ComparedwithotherMCPalgorithms,theoptimizedalgorithmhaslesserrorinwindspeed,Kvalueandpowergenerationcalculati