数据流技术及其在电力信息处理中的应用研究

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1、华北电力大学（保定）博士学位论文数据流技术及其在电力信息处理中的应用研究姓名：孔英会申请学位级别：博士专业：电工理论与新技术指导教师：苑津莎20081201华北电力大学博士学位论文摘要AbstractDataStreamManagementtechniqueshavebecomehotspotsinthefieldsofinformationprocessinganddatabase,itc锄provideeffectivesupportforreal—timeinformationprocessingandanalysis．Witllt

2、heexpansionofpowergridsandtheraisingofautomationlevel，largeamountofreal-timedatageneratedbythepowersystemoperationformsdatastreams，thesedatastreamsincludeimportantinformationrelatedwithequipmentfailuresandsystemstability．Studyondatastreamtechniquesanditsapplicationinelect

3、ricpowerinformationprocessinghasimportanttheoreticalvalueandpracticalvalue．The’maincontentofourresearchincludes：Ageneraldatastrealnprocessingmodelisestablishedaccordingtothereal·timerequirementfordatastreamprocessing,itcanbeusedtoprocessrecentdatausingslidingwindow，andcal

4、lbesuitablefordifferentprocessingneedsbywayofsynopsisdatastructureanddatastreamprocessingalgorithm．Howtoestablishasynopsisdatastructureusingwavelettransfomasandupdatetheresultsincrementallyisinvestigated．Animprovedincrementalupdatealgorithmforwaveletdecompositionispropose

5、d,whichcallimprovethehandlingaccuracyandreducetheresponsetime．’Anewmethodforconstructingasynopsisdatastructurebasedonparametersestimationisproposedtomeettheneedofsystemidentificationapplications，whichprovideanewwayfortheconstructionofdatastreamssynopsis．Asimulationexperim

6、entofthetransformerfaultdiagnosisusingtemporalcharacteristicsandparametersestimationalefinished，theexperimentalresultsshoweffectivenessofproposedmethod．111eissuesofcontinuousqueryandanomalydetectionindatastreamandrelatedproblemsareanalyzed．Theanomalydetectionalgorithmbase

7、donshiftedwavelettreeindatastreamshavebeenstudied，andallimprovedalgorithmwasproposed．Fortheimprovedalgorithmofshiftedwavelettree，monotonoussearchspacewasconstructedforbinarydetectionwhichhasimprovedthedetectionefficiency；．andtheincrementalalgorithmofupdatingWavelettreeisu

8、setoreduceresponsetime．。Asimulationexperimentofdetectingvoltagesagwasfinished，theexperimentalres