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https://hdl.handle.net/20.500.12104/90884Registro completo de metadatos
| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.contributor.author | Sandoval Guzmán, Betsy | |
| dc.date.accessioned | 2022-09-12T22:10:06Z | - |
| dc.date.available | 2022-09-12T22:10:06Z | - |
| dc.date.issued | 2022-04-07 | |
| dc.identifier.uri | https://wdg.biblio.udg.mx | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12104/90884 | - |
| dc.description.abstract | This thesisexplorestheadvantagesoforganizingpowersystemsdataintensorar- raysandusingtensordecompositionstoprocesstheinformationoverthetraditional wayin2Darrays(matrices).Todoso,thisresearchworkreformulatessomewell- knownpowersystemproblems,suchasdataclustering,datavisualizationanddata compression forsmartmeterandPMUdata,ScreeningContingencyandCoherence Identication.This,undertheassumptionthatmanymeasureddatainthepower system canhaveamultidimensionalrepresentationintoatensor,whichcananal- ysed usingmethodsbasedontensordecomposition.Todemonstratetheadvantages of workingwiththesemethods;alongthisthesisfourdataminingmethodologies are proposedtounderstandingofthepowersystemdatacollectedbythemoderns monitoring systems: 1)An unsuperviseddataminingalgorithmisproposedforhomogeneoussmartgrid data, particularlyforelectricalloadprolesusingParallelFactorAnalysis(PARAFAC) tensor decomposition.Sincetheproposedtensorrepresentationallowstoassigna givendimensiontoaparticularvariableinvolved;datareduction,datacompression, data visualizationanddataclusteringarearchivedseparatelyforeveryvariable. 2)Data CompressionofPowerSystemsInformationfromAdvancedMonitoringIn- frastructure suchasPhasorMeasurementUnits(PMUs)andtheSmartMeter(SMs) is proposedusingTuckertensordecompositiontoachieveahighcompressionratio and alowreconstructionerror. 3)Screening ContingencyandCoherencyIdenticationarefacedsimultaneously ii based onPARAFAC-2tensordecomposition.Todoso,thetemporalandgeographical information isextractedfromatensorarraywithmultiplescontingencies. 4)A hybridclusteringalgorithmforelectricalloadprolesconsideringweather variablesisproposed.Theproposedalgorithmisbasedontheideaofunifyingcost functions resultingfromthedimensionalityreductionformulationofmultidimensional arrays. The resultsinthefourproposedmethodologiesshowthatthereformulationof the problemsyieldsmoreinformationabouttheeventstudiedthanwithtraditional matrix-based approaches.Thusdemonstratingthattensordecompositionisatool with greatpotentialfordataanalysisinelectricpowersystems. | |
| dc.description.tableofcontents | 1 Introduction 1 1.1 Motivation . ................................ 1 1.2 Objectives . ................................ 4 1.3 Hypothesis . ................................ 5 1.4 StructureoftheThesis . ......................... 5 2 TheoryofTensorDecomposition 7 2.1 Introduction . ............................... 7 2.1.1 NomenclatureandDenitionofTensor . ............ 8 2.1.2 TwoDimensionalDataRepresentation(MatrixRepresentation) 9 2.1.3 MultidimensionalDataRepresentation(TensorRepresentation) 9 2.2 MathematicalBackground . ....................... 10 2.2.1 Kroneckerproduct . ....................... 10 2.2.2 Khatri-Raoproduct . ....................... 11 2.2.3 The n-mode product:Tensormultiplication . ......... 11 2.2.4 UnfoldingProcess . ........................ 11 2.2.5 Rank-onetensor . ......................... 11 2.2.6 Sub-arraysofa3DTensor:FibersandSlices . ......... 12 2.2.6.1 Fibers . ......................... 12 2.2.6.2 Slices . ......................... 13 2.3 SelectedTensorDecompositions . .................... 13 2.3.1 PARAFACTensorDecomposition . ............... 13 2.3.1.1 AnalysisofthePARAFACtensordecompositionand the \PrincipleofParallelProportionalProles . .. 15 2.3.2 TuckerTensorDecomposition . ................. 16 2.3.2.1 AnalysisoftheTuckertensordecompositionandthe core tensor . ...................... 18 2.4 PARAFAC-2Decomposition . ...................... 19 2.5 ConclusionsofChapter . ......................... 20 3 AnUnsupervisedDataMiningApproachforHomogeneousElectri- cal LoadProles 21 3.1 Introduction . ............................... 21 3.2 StateoftheArt . ............................. 23 3.3 InterpretationofPARAFACtensordecompositionforSmartMeterdata 25 3.3.1 AnalysisofslicesandbersasresultofPARAFACmodelde- composition . ........................... 26 3.4 AddedvalueofPARAFACinElectricalPowerSystems . ....... 27 3.5 MainResults . .............................. 28 3.5.1 TensordesignfortheERCOTsystem . ............. 29 3.5.2 PARAFACTensordecompositionoftheERCOTsystem . .. 30 3.5.2.1 DataCompression . .................. 30 3.5.2.2 Datavisualizationanddataclustering . ....... 31 3.5.3 DataReconstruction:Missingdata . .............. 35 3.6 AnalysisofComputationalComplexity . ................ 37 3.7 Conclusion . ................................ 39 4 DataCompressionforAdvanceSensingandCommunicationTech- nology inSmartGrids 40 4.1 Introduction . ............................... 40 4.2 StateoftheArt . ............................. 42 4.2.1 CompressionmethodsforPMUdata . ............. 43 4.2.2 CompressionMethodsforSmartMeterdata . ......... 45 4.3 DatacompressionbasedonTuckerdecomposition . .......... 46 4.3.1 ComputingTuckertohandlemissingdatausingHigher-Order Orthogonal Iteration(HOOI) . ................. 48 4.4 ApplicationsusingSmartGridData . ................. 49 4.4.1 CaseStudy1:datacompressionofPMUmeasurments . ... 50 4.4.1.1 PMUdatacompressionbasedonTuckerdecomposi- tion . .......................... 53 4.4.1.2 PMUdatacompressionbasedonSVD . ....... 55 4.4.2 CaseStudy2:datacompressionofSmartMeterdata . .... 56 4.4.2.1 SMdatacompressionbasedonTuckerdecomposition 57 4.4.2.2 SMdatacompressionbasedonSVD . ........ 59 4.5 ConclusionsoftheChapter . ....................... 61 5 ScreeningContingencyandCoherencyIdenticationinPowerSys- tems 62 5.1 Introduction . ............................... 62 5.2 TwoDimensionalDynamicFeatureExtraction . ............ 64 5.3 MultidimensionalDynamicFeatureExtraction . ............ 65 5.3.1 MultidimensionalRepresentation(TensorRepresentation) . . 65 5.3.2 Extractionofthespatialandtemporalinformationfromthe proposedtensor . ........................ 65 5.3.3 SeverityIndexbasedontemporalinformation . ........ 67 5.4 NumericalResults . ............................ 67 5.4.1 TestCase . ............................ 68 5.4.2 ContingencyScreening . ..................... 69 5.4.3 IdenticationofCoherentAreas . ................ 71 5.5 Discussion . ................................ 72 5.6 ConclusionsChapter . .......................... 73 6 ClusteringofCoupledArraysonPowerSystemsUsingaHybrid DecompositionApproach 75 6.1 Introduction . ............................... 75 6.1.1 Summaryofcontributions . ................... 76 6.2 ClusteringofIndividualArrays . .................... 77 6.2.1 Clusteringofindividualvariablesstoredinmatrixarrays:In- trinsic approach . ......................... 77 6.2.2 Clusteringofcombinedvariablesstoredintoatensor:Extrinsic approach . ............................. 79 6.3 ClusteringofCoupledArrayswithCombinedVariables:HybridAp- proach . .................................. 80 6.3.1 Reconstructionofvariablesafterdecomposition . ....... 82 6.4 IllustrativeExampleUsingSyntheticData . .............. 82 6.5 DemonstrationUsingSmartGridData . ................ 85 6.5.1 Testcaseandconstructionofcoupledarrays . ......... 86 6.5.2 Mainresults . ........................... 86 6.5.3 Comparisonusingclusteringofindividualarraysanddetection of Atypicaldays . ......................... 89 6.5.3.1 Comparisonusingonlyinternalvariables . ...... 89 6.5.3.2 Comparisonusingonlyexternalvariables . ...... 90 6.5.3.3 Detectionofatypicaldays . .............. 92 6.6 ConclusionsoftheChapter . ....................... 93 7 ConclusionsoftheThesis 94 7.1 GeneralConclusions . .......................... 94 7.2 GeneralKeyContributions . ...................... 94 7.3 FutureWork . ............................... 96 A Appendix 97 A.1 SingularValueDecomposition(SVD) . ................. 97 A.2 PrincipalComponentAnalysis(PCA) . ................. 98 | |
| dc.format | application/PDF | |
| dc.language.iso | spa | |
| dc.publisher | Biblioteca Digital wdg.biblio | |
| dc.publisher | Universidad de Guadalajara | |
| dc.rights.uri | https://www.riudg.udg.mx/info/politicas.jsp | |
| dc.title | ANÁLISIS DE DATOS EN SISTEMAS ELÉCTRICOS DE POTENCIA MEDIANTE TÉCNICAS BASADAS EN MÉTODOS DE DESCOMPOSICIÓN TENSORIAL | |
| dc.type | Tesis de Doctorado | |
| dc.rights.holder | Universidad de Guadalajara | |
| dc.rights.holder | Sandoval Guzmán, Betsy | |
| dc.coverage | GUADALAJARA, JALISCO | |
| dc.type.conacyt | doctoralThesis | |
| dc.degree.name | DOCTORADO EN CIENCIAS DE LA ELECTRONICA Y LA COMPUTACION CON ORIENTACIONES | |
| dc.degree.department | CUCEI | |
| dc.degree.grantor | Universidad de Guadalajara | |
| dc.rights.access | openAccess | |
| dc.degree.creator | DOCTOR EN CIENCIAS DE LA ELECTRONICA Y LA COMPUTACION CON ORIENTACIONES | |
| dc.contributor.director | Barocio Espejo, Emilio | |
| Aparece en las colecciones: | CUCEI | |
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|---|---|---|---|
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