基于混沌理论和小波变换的电力系统短期负荷预测

《基于混沌理论和小波变换的电力系统短期负荷预测》由会员上传分享，免费在线阅读，更多相关内容在学术论文-天天文库。

1、ABSTRACTWiththedevelopmentofpowermarket，thepowerloadforecastingplaysamoreimportantrolethaneverinpowersystem．Inpractice，theplanning、designingandthedispatchingautomationofpowersystemrequireloadforecastinggreatly．Theresearchofloadforecastingattractsmorepeople’Sattentionandhasbeenanimportantfieldofmorde

2、npowersystem．Powersystemisaverys仃ongnonlinearsystem,andappearsaschaosbehavior．Thispaperreviewsandcommentsthetheoriesandmethodsofthepresentlyelectricalloadsprediction,andinlroducetheapplicationanddevelopmentofchaostheory．Combinedwiththeprocessingmethodofchaotictimesseries，thisdissertationpresentshigh

3、erforecastingprecisionmethodthroughwaveletsingularitydetectionandwaveleteliminatingnoise．Robustnessofthelocallinearregressionmethodispoorbecausetheillness-statematrixissensitivetonoisewhenthemodelislinearized．Anadaptive··selfpredictionfilterwasproposedtosolvetheinfluenceofillness··statematrix．nledis

4、sertationmainfoCUSOnfollowsitem：’Historicalloaddateisthebasisofloadforecastingandloadfeatureanalysis．Falsedataandnoisesamongloaddatawilldisturbloadforecastingandloadfeatureanalysis．ThispaperillustratedtheinfluenceofdifferentlevelnoiseonpredictaccuracythroughthechaoticsystemofLogisticmap．Sotheloaddat

5、emustbecorrectedandsmoothedbeforeusingespeciallyforchaoticmethod．ProcessedbythemethodthroughadjustingamplitudeoftheirwaveletmodulusmaximaandprocessingthewaveletdecomposeddetailsignalbysoftRigorousSUREthresholdbasedonwaveletanalysisandsingularitytheory,faultdatebeeliminated．Therealhistoricalinformati

6、onandregulationdataCanbegainedforloadforecasting．Atthesametimenoisesareremoved．Thevalidityofthemethodisprovedbytheapplicationintheone-ranklocal-regionmethod．Locallinearregressionmethodwaswidelyusedinchaoticpowersystemshorttermloadforecasting．Buttherearetwoshortagesinthemethod．First,thepredictionaccu

7、racyissensitivetotheembeddingdimensions．Thepredictionerrorbecomelargerandthealgorithmbecomeunstableiftheembeddingdimensionisnotcorrect．Second,Robustnessofthemethodispoorbecausetheillness—statematrixis