The%20identification%20and%20filling%20of%20wind%20turbine%20anomaly%20data%20and%20missing%20data%20is%20of%20great%20significance%20for%20the%20assessment%20of%20the%20operating%20state%20of%20the%20wind%20turbine%20and%20the%20prediction%20of%20future%20wind%20speed.%20This%20paper%20considers%20that%20some%20wind%20turbines%20in%20SCADA%20system%20may%20have%20abnormal%20data%20and%20a%20large%20amount%20of%20missing%20data.%20Firstly,%20the%20data%20is%20identified%20and%20excluded,%20and%20then%20classified.%20For%20the%20case%20of%20missing%20individual%20missing%20points,%20fill%20with%20the%20mean%20of%20adjacent%20data;%20In%20the%20case%20of%20continuous%20missing%20and%20side%20wind%20turbine%20data%20reference,%20first%20establish%20the%20wind%20direction%20filling%20model,%20draw%20continuous%20and%20complete%20wind%20direction%20data,%20and%20then%20use%20the%20SVM%20method to establish%20the%20wind%20speed%20filling%20model%20in%20each%20wind%20direction%20interval%20respectively%20based%20on%20the%20adjacent%20wind%20turbine%20data%20at%20the%20same%20time.%20For%20the%20missing%20data%20without%20the%20side%20wind%20turbine%20reference,%20the%20NAR%20neural%20network%20is%20used%20for%20point-by-point%20wind%20speed%20filling.%20In%20this%20paper,%20the%20measured%20data%20of%20a%20certain%20wind%20field%20is%20used%20for%20data%20verification,%20and%20compared%20with%20other%20traditional%20neural%20network%20filling%20methods.%20The%20test%20results%20show%20that%20the%20proposed%20method%20outperforms%20other%20models.
