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- W2183180699 abstract "raw measurements. Baddata processing isan essential function ofa stateestimator for detection, identification, andremoval ofbaddatainthe measurementset.Thispaper concernsitself mainly withbad dataidentification forleast-squares estimation ofthepower system static state. Conventional baddataidentification inPSSEhaveall resorted tosuchstatistics as theabsolute values ofthe normalized orstudentized residuals. Oncethevalue ofsucha statistic exceeds certain specified threshold, thecorrespond¬ ing dataisflagged as bad.Itisa well-known factthat whenmultiple interacting baddataarepresent useofsuch statistics alonemayleadtounidentification ofbaddataor misidentification ofgood data. Thispaperproposes a new multiple baddataprocessing procedure whichintroduces hypothesis teststocorrect misidentifications causedbyuse oftheconventional testing statistics. Afterbaddataare detected, a suspected datalist isdrawnbytesting first the studentized andthenthenormalized residuals against speci¬ fied thresholds. Thedataindexed bythesuspected datalist are deleted fromthemeasurementsetandthepredicted residuals ofthedeleted dataarecomputed. Hypothesis tests on themeans ofthepredicted residuals will thendetermine statistically ifthecorresponding datacontain gross errors. Thedeleted good dataarevalidated there andreturned tothe measurementset.Theoverall procedure worksbycycles. Alsoproposed isa single baddatadetection mechanism whichaddstotheefficiency ofbaddataprocessing. Central totheproposed procedure aretheconcepts oftheperfect dataestimator andthepredicted residuals. Perfect DataEstimator andthePredicted Residuals Theconcept oftheperfect dataestimator istoisolate a subset ofgood datawhereby thevalidity ofother datacanbe examined. Thedataset,D,can bepartitioned, bysome rule, into subsets GandS.Ideally, thedatainGareassumed tobe freefromgross errorswhilethoseinS aresuspected tobe bad.A new stateestimate, %g, can becomputed using only G.Theresiduals ofthedatainScan thenbeevaluated atXg. Thesearecalled thepredicted residuals. Inourimplementa¬ tion, setG isobtained bydeleting thesuspected datafrom themesaurementset.Thedeleted databelong toS.Since the datainG arenotcontaminated bygross errors,%gisclose to thetruestate, x. Thepredicted residuals thusreflect the deviations ofthedatainS fromtheir truevalues. Large predicted residuals flag baddata. Inparticular, letrs(j) denote thepredicted residual ofthe/th data(/E SI, itisproved that rs(j)~N(b?, (of +vjj)) with" @default.
- W2183180699 created "2016-06-24" @default.
- W2183180699 creator A5086806236 @default.
- W2183180699 date "1987-01-01" @default.
- W2183180699 modified "2023-09-26" @default.
- W2183180699 title "BadDataProcessing inPower System State Estimation byDirect DataDeletion andHypothesis Tests" @default.
- W2183180699 hasPublicationYear "1987" @default.
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