金风科技风资源技术专家杨长锋：测风数据长期订正对风资源评估的影响

1©GOLDWINDSCIENCE&TECHNOLOGYCO.,LTD.测风数据长期订正对风资源评估的影响Theimpactoflong-termcorrectionofwindmeasurementdataonwindresourceassessment北京金风科创风电设备有限公司BEIJINGGOLDWINDSCIENCE&CREATIONWINDPOWEREQUIPMENTCO.,LTD风资源技术部WindResourceAssessmentDepartment高级风资源工程师：杨长锋SeniorWindResourceEngineer：ChangfengYang长期订正的作用及意义Thefunction

andsignificanceoflong-termrevision01算法汇总Algorithmsummary02结果验证Results03结论及相关进展Conclusion&Relatedprogress04CONTENTS目录3长期订正的作用及意义Thefunctionandsignificanceoflong-termrevision数据清理参数计算风速风频分布极端风速入流角湍流切变水平外推垂直外推降低LCOE计算的不确定度，为项目评估提供支持提高载荷输入参数准确性算法汇总AlgorithmsummaryAlgorithmTypeAbbrev.DescriptionLinearLeastSquaresLLSTheclassicleastsquaresfittothescatterplotoftargetandreferencespeedsTotalLeastSquaresTLSAslightmodificationofLLSthatminimizesorthogonaldistancetothebestfitVerticalSliceVSApiecewiselinearfittotthescatterplotoftargetandreferencespeedsVarianceRatioVRAsimplelinearmappingthataimstopreservethevarianceofthetargetdataBulkSpeedRatioBSRThesimplestpossiblealgorithm,basedontheratioofmeanwindspeedsWeibullFitWBLApowerlawfitwhoseparametersderivefromtheWeibullparametersofthetargetandreferencedataSpeedSortSSAlinearfittotherelationshipbetweentargetandreferencecumulativefrequencydistributionsMatrixTimeSeriesMTSAnimplementationoftheclassicmatrixmethodthatwemodifiedtoproducerealistictimeseriesdata整体最小二乘TotalLeastSquaresAlgorithm线性最小二乘LinearLeastSquaresAlgorithm分风速段线性回归VerticalSliceAlgorithm方差比值法VarianceRatioAlgorithm风频拟合法WeibullFitAlgorithm速度追踪法SpeedSortAlgorithm风速序列联合概率分布MartixTimeSeriesAlgorithmCASE1CASE2CASE3CASE4Duration6years6years3years3yearsR^20.6440.5750.6210.598RecoveryRate99.29%99.93%99.69%99.54%Timestepsofcomparison60min60min60min60mintimes212199评价指标WSKWPDAEPSD√？☆★RMSE√？☆★计算结果及验证思路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时间长度对计算结果的影响Theeffectoflengthofdataonthecalculationresults结果验证-风速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结果验证-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结果验证-风功率密度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结果验证-发电量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季节因素对长期订正的影响十分重要，随测风时间增加，风速订正不确定度会明显降低。Theinfluenceofseasonalfactorsonlong-termcorrectionis

important.Asthewindmeasurementtimeincreases,thewindspeedcorrectionuncertaintywillbesignificantlyreduced.在进行长期订正时，不仅要注意风速的变化值，同时要注意K值的变化幅度，避免造成风功率密度产生较大差异。Inthelong-termcorrection,toavoidalargedifferenceinthecalculationofthewindpowerdensity.PayattentiontothevariationoftheKvaluewhilepayingattentiontothechangevalueofthewindspeed.结论Conclusion线性回归方法在风速订正上与其他方法差异不大，但会较大幅度改变K值，对发电量计算产生较大影响。尤其是实测风速K值较小时，误差会更加明显。Thelinearregressionmethodhaslittledifferencewithothermethodsinwindspeedcorrection,butitwillchangetheKvaluegreatly,whichhasagreatimpactonthecalculationofpowergeneration.EspeciallywhenthemeasuredwindspeedKvalueissmall,theerrorwillbemoreobvious.优化后的算法与其他MCP算法相比，在风速、K值和发电量计算上误差较小且表现较为稳定。ComparedwithotherMCPalgorithms,theoptimizedalgorithmhaslesserrorinwindspeed,Kvalueandpowergenerationcalculati