A cognitive system for fault prognosis in power transformers.

ContentslistsavailableatScienceDirect

Electric

Power

Systems

Research

jou rn al h om e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / e p s r

A

cognitive

system

for

fault

prognosis

in

power

transformers

Fernando

Cortez

Sica

,

Frederico

Gadelha

Guimarães

,

Ricardo

de

Oliveira

Duarte

_,

Agnaldo

J.R.

Reis

a_{GraduatePrograminElectricalEngineering–FederalUniversityofMinasGerais–Av.AntônioCarlos6627,31270-901BeloHorizonte,MG,Brazil} b_{DepartmentofComputerScience,FederalUniversityofOuroPreto(UFOP),OuroPreto,Brazil}

c_{DepartmentofElectricalEngineering,UniversidadeFederaldeMinasGerais(UFMG),BeloHorizonte,Brazil} d_{DepartmentofElectronics,UniversidadeFederaldeMinasGerais(UFMG),BeloHorizonte,Brazil}

e_{DepartmentofControlEngineeringandAutomation,FederalUniversityofOuroPreto(UFOP),OuroPreto,Brazil}

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received6February2015

Receivedinrevisedform30April2015 Accepted23May2015

Availableonline11June2015 Keywords: Powertransformers Knowledge-basedsystems Cognitivesystems Faultprognosis Faultdiagnosis DissolvedGasAnalysis

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b

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Thepowertransformerisoneofthemostcriticalandexpensiveequipmentsinanelectricpowersystem. Ifitisoutofserviceinanunexpectedway,thedamageforbothsocietyandelectricutilitiesisvery significant.Overthelastdecades,manycomputationaltoolshavebeendevelopedtomonitorthe‘health’ ofsuchanimportantequipment.TheclassificationofincipientfaultsinpowertransformersviaDissolved GasAnalysis(DGA)is,forinstance,averywellknowntechniqueforthispurpose.Inthispaperwepresent anintelligentsystembasedoncognitivesystemsforfaultprognosisinpowertransformers.Theproposed systemcombinesbothevolutionaryandconnectionistmechanismsintoahybridmodelthatcanbe anessentialtoolinthedevelopmentofapredictivemaintenancetechnology,toanticipatewhenany equipmentfaultmightoccurandtopreventorreduceunplannedreactivemaintenance.Theproposed procedurehasbeenappliedtorealdatabasesderivedfromchromatographictestsofpowertransformers foundintheliterature.Theobtainedresultsarefullydescribedshowingthefeasibilityandvalidityof thenewmethodology.TheproposedsystemcanhelpTransformerPredictiveMaintenanceprogrammes offeringalowcostandhighlyflexiblesolutionforfaultprognosis.

©2015ElsevierB.V.Allrightsreserved.

1. Introduction

Powertransformersareconsideredkey-elementsforthe Elec-tric Utilities (EU). When those equipments fail, households, industries,andhospitals,tonameafew,arepronetosuffer some-how.Besides,anunplannedinterruptioninthepowersupplycan betranslatedintoheavyfinesfortheEU.Hence,toolsfordiagnosis, faultdetectionandfaultprognosisarerequired.Inthecontextof powertransformers,severalstudiesarenoteworthyregardingthe aspectsofprotection,monitoringanddiagnostics,seeforinstance [1–5].

Formanyyears,preventivemaintenanceprogrammesinpower transformersconsistedof inspections,testsand actionsin peri-odic time intervals usually suggested by the manufacturers or determinedthroughpracticalexperience.Itwasalsocommonthe applicationofroutinetestsandproceduressuchas:measurement

∗ Correspondingauthor.Tel.:+553134093419. E-mailaddresses:[email protected]

(F.C.Sica),[email protected](F.G.Guimarães),[email protected] (A.J.R.Reis).

ofdielectriclosses,insulationresistanceandwindingresistance; physical–chemicalanalysisandchromatographicoilanalysis; man-ual or automaticmonitoring of temperature [6].Such analyses allowedtheoperatorstoverifyifagiventransformerwas operat-ingnormallyoriftherewereevidencesofthermaland/orelectrical failures,forinstance.Thesekindoffailuresstemfromnaturalwear, environmentalactionsandoverloads,amongothercauses. Refer-enceworkinthisareacanbefoundin[6–8].

Amongseveralfaultdetectionmethods,manyfaultsthatoccur inpowertransformerscanbedetectedifonemeasuresthegases concentrationsintheirinsulatingoil.Thisprocedureisknownas faultdetectionviaDissolvedGasAnalysis(DGA)[6].Usually,DGA canbecarriedoutintwomodes:off-lineand on-linemodes.In theoff-linemode,thepowertransformerhastobedisconnected fromthepowersystemandanoilsampleiscollectedandtakento alaboratorywhereitwillbeanalysedviaagaschromatography technique.Yetintheon-linemode,thepowertransformiskept connectedtothepowersystemandtheDGAisperformedinloco withadeterminedtimeinterval(e.g.every2h)using,e.g.,a com-pactclosed-loopgaschromatographunit,whichismountedonor nearthemonitoredtransformer.Techniquessuchasopticaland chromatography[9–11],electrical–chemicalsystems[12,13]and http://dx.doi.org/10.1016/j.epsr.2015.05.014

Fig.1. Duval’sTriangleMethod.

near-IRabsorptionspectroscopy[14]canbeemployedintheDGA aswell.

Amethodforfaultclassificationinpowertransformersnamed Duval’sTriangleMethod(DTM)isintroducedin[6]anddescribedin IEEEC57.104[7]andinIEC60599[8](seeFig.1).DTMisbasedon thelevelsofthethreedissolvedgasesfoundinthepower trans-formeroil:ethylene(C2H4),methane(CH4)andacetylene(C2H2). TheDGAbyDTMinvolvesthepercentagesoftheaforementioned gasesratios,seeEqs.(1)–(3),presentedinagraphicalform.The threesidesofthetriangleincartesiancoordinatesx,yandz repre-senttherelativeproportionofCH4,C2H4andC2H2,from0to100% foreachgas.Theintersectionsofallthreegasesratiosindicatethe kindoffaultpresentedbythetransformer.Alltypesoffaultsinthe DuvaltrianglecanbefoundinTable1.

InEqs.(1)–(3),xistheconcentrationofCH4 inppm,yisthe concentrationofC2H4inppmandzistheconcentrationofC2H2in ppm. CH4(%)=100 x (x+y+z) (1) C2H4(%)=100 y (x+y+z) (2) C2H2(%)=100 z (x+y+z) (3)

Takingintoconsiderationthat therearenotefficient mathe-maticalmodelstodescribetherelationshipbetweentherateof evolutionoftheseconcentrationsand thefailures,and the pro-cessofgatheringhistoricaldataisacommonpracticenowadays, thedevelopmentofpattern classifiersbasedonSupportVector Machines[15–17],FuzzyLogicapproach[18],Neuro-Fuzzy mod-els[19,20],Wavenets[21,22],NeuralNetworks[23,24],stochastic Petrinetbasedmethodology[25],probabilisticclassifierbasedon particleswarmoptimizer[26],DecisionTrees[27–29],and boot-strapandGeneticProgramming[30]hasreceivedagreatdealof Table1

FaultsdescribedbytheDuval’sTriangleMethod(DTM).

Nomenclature Typeoffault

PD Partialdischarge

D1 Lowenergydischarge

D2 Highenergydischarge

DT Mixofthermalandelectricalfaults

T1 ThermalfaultwithT<300◦_C

T2 Thermalfaultwith300<T<700◦_C

T3 ThermalfaultwithT>700◦_C

• thevastmajorityofpapersinthisfielddealwithoff-lineDGA. • theauthorsfocusedonfaultdiagnosisinpowertransformers.

Faultprognosiswasnotamajorconcern.Incidentally,thislatest strategyisrelativelynew.Refs.[32–36]arefewexceptionsinthis particularsubject.

Itisinthiscontextthatwepresentanewcomputational sys-temcapableofcarryingoutfaultprognosisinpowertransformers. Thenewapproachisbasedonahybridsystemformedby connec-tionistandevolutionarymechanisms,whichwasappliedtoreal databasesderivedfromchromatographtestsofpower transform-ersfoundintheliterature[37].Insteadofinformingthecurrent stateofthetransformeronly,e.g.NormalCondition(NC),Electrical Fault(EF),ThermalFault(TF),thefaultprognosistoolwillestimate –fromagivencurrenttransformerstate–ifthetransformerunder analysisislikelytopresent,forinstance,athermalfailureinsix monthsandhowthefailurewillevolvethroughtime.Suchtoolis ofinterestoftheEUbecauseitcanbecomeanessentialtoolfor theTransformerPredictiveMaintenance(TPM).TPMcanbeseen asamaintenancestrategythatprovidestransformerfailureearly detectionandisabletorecognizeconditionsthatleadtodefects. Ideally,TPMshouldallowthemaintenancefrequencytobeaslow aspossiblepreventingunplannedreactivemaintenanceand reduc-ingunnecessarypreventivemaintenance.Theproposedprognosis systemis anintelligent systemthat wasimplemented withan architectureformodellingcognitivesystems.

Themaincontributionsofthisworkare:(i)propositionofan on-lineintelligentfaultprognosissystemforpowertransformersthat iscapableofpredictingwhenandwhichtypeoffaultisexpectedto occur.TheproposedsystemcanhelpTPMprogrammesofferinga lowcostandhighlyflexiblesolutionforfaultprognosis; addition-allytheprognosissystemmanipulatesthegasesconcentrationsat thesametimeasmultipleinputstothesystem.(ii)Propositionof ageneralarchitectureforcognitiveandknowledgebasedsystems, onwhichthefaultprognosissystemisimplemented.

2. Faultprognosissysteminpowertransformers

Prognosis(alsoPrognostics)isanengineeringdiscipline involv-ingthepredictionofwhenasystemorcomponentwillnolonger performasintendedand,ifpossible,classifywhichkindoffaultwill occur[38–41].ThepredictedtimeistermedRemainingUsefulLife (RUL),whichisanimportantconceptincontingencyandinSystem HealthManagement(SHM).Prognosissystemscanbecategorized intodata-drivenmethodsandmodel-basedmethods[43].Inany case,themethodrequiressomeinitialknowledgeaboutamodelof thesystemorcomponent;orknowledgeintheformofinformation aboutpreviousfaultcasesandconditions.

Data-drivenprognosissystemsusuallyemploypattern recog-nitionandmachinelearningtechniquestodetectchangesinthe states of the system of interest or changes in the monitored variables.Theyarerecommendedwhen thereisnoappropriate understandingofthephysicsbehindtheoperationofthesystem, ortheprinciplesrelatingitsoperationtofaultconditions.

IndustrialapplicationsofPrognosis arediverse, rangingfrom manufacturing, automotive and aerospace industries and also powergenerationanddistribution.Inthepowergeneration indus-try,commercialapplicationsofSHMincluderotatingmachinery [43]andwindturbines[44].Renewableenergyapplications,such aswindturbines,canalsobenefitfromSHMtechnologybasedon

Fig.2. Cognitiveprocess.

prognosissystems.Approachesbasedonstatisticalmethodsand machinelearningcanbefoundin[45,46].

In[47],theauthorsproposeanensembleofdata-driven prog-nosticalgorithmsforrobustpredictionofRULbytheweightedsum oftheoutputsoffivedifferentmethods:aSimilarity-Based Interpo-lation(SBI)approachwithRelevanceVectorMachine(RVM)asthe regressiontechnique,SBIwithSupportVectorMachine(SVM),SBI withtheleast-squareexponentialfitting,aBayesianlinear regres-sionwiththeleast-squarequadraticfitting,andaRecurrentNeural Network(RNN)approach.Specificallyonfaultprognosisinpower transformers,itispossibletofindsomefewrecentstudiesinthe literature,as the onesdescribed in [35,36]. In [35],the author utilizesBayesianParticleFilteringtoestimatetheRULofthe trans-former,consideringthedegreeofpolymerizationoftheinsulating materialandtemperaturemeasurementsinspecificpointsofthe transformer(hotspots).In[36],anAdaptiveNetwork-basedFuzzy InferenceSystem(ANFIS)isusedtoinferchangesinthedissolved gasconcentrations.OneANFISisdefinedforeachgassuchthat spe-cificfuzzyrulesareappliedforthedynamicsofeachgasconsidering alsotheoperationtimeofthepowertransformer.

3. Proposedcognitivesystemforfaultprognosis

3.1. Cognitiveandintelligentsystems

Whenconsideringknowledgebasedcomputationalmodelsand systems,asthefaultdetectionandpredictionsystemproposedin thispaper,thefirststepisthedescriptionandmodellingofthe knowledgeandconsequently,howthisknowledgeisactually rep-resentedandstored.Itisinterestingtonoticethatthecognitive processisadynamicone.Thisdynamicsisrelatedtoitsinteraction withtheenvironment:collectinginformation,coding,storingand processingthisinformationsuchthatsomeactioninthe environ-mentcanbeperformed(Fig.2).

ArtificialCognitiveSystemsaresystemsdesignedwithmachine learningtechniques.Theyhavethecapacityofrepresentingand manipulatingknowledgetoassistorimprovehumanexpertiseand cognition,toperformcomplexdecision-makingandactioninthe environment.Anarchitectureforcognitivesystemsisactuallya blueprintforintelligentagents.Anyproposedarchitectureshould providefunctionalitiesthatfacilitatetheproposition ofamodel foracomputationalsystemtoactasanintelligentsysteminsome sense.

Therefore,ingeneral,themodellingofacognitiveandintelligent systemshouldinvolvethefollowingbasiccomponents:

• Sensorysystem;

• Storage,codingandprocessingsystem; • Actuationsystem;

• Communicationsystem.

Thegoalofthissectionistodescribetheproposedsystem mod-ellingandimplementationaspects.Themodellingisdescribedas genericaspossible,makingitusefultootherapplicationsinvolving knowledgebasedsystems,althoughthefocushereisonthepower transformerfaultprognosissystem.

Fig.3.Basicarchitectureofthesystem.

3.2. Proposedarchitecture

Thefaultprognosissystem(FPS)isdevelopedonageneral pro-posedarchitectureformodellingcognitivesystems.Thedesigned systemcombines connectionist and evolutionarymodelsinto a knowledge basedsystem,specifically,anautomaticdata-driven prognosissystem.

In generalterms, theproposedarchitecturefor the develop-mentofcognitivesystemsusestheideaofautonomousintelligent agents for clustering of information and processing of classes. Thisclusteringallowsabetterrepresentationandorganizationof theknowledgeincorporatedandmanipulatedbytheagentsand providesbetterscalability. Anotherreason forthe clusteringof informationistoallowtheseparationofagentsintermsof func-tionalclasses,consideringthekindofknowledgetoberepresented and the type of actuationrequired. In this way, higher system granularitycanbeachievedfortheimplementationofmore com-plexsystemsapplications,suchasforinstancehealthmonitoring systemsasdescribedin[47].Theproposedarchitectureisa multi-agentsystemthatallowstheuseofdifferenttechniquestogether inanensembleapproach,forexample,employingBayesian net-works,fuzzymodelsandstatisticalmodels.Thesystemisflexible enoughintermsofscalabilitytoincludenewmethodsandhybrid approachesforcomplextasks.

Fig.3showsamacroscopicviewofthesystemwithmultiple clustersinstantiated.Eachclusterhasthreebasicandfixed ele-ments:(i)EmbryonicAgent,(ii)CommonClusterMemoryand(iii) IntraClusterBus,withtheirrespectiveagentinstances.

TheEmbryonicAgentmanagesthecreationorremovalofagent instances. Thismechanismallows fortheself-adaptationof the multi-agentsystemgiventheenvironment dynamics.The Com-monClusterMemoryisacommonareaforlongterminformation storage(longtermmemory),thatis,informationsuchas configura-tion,characterizationofincorporatedknowledgeandcodification relatedtotheactuationprocess.TheIntraClusterBusandthe Inter-ClusterBusarecommunication elementsrepresentedbystorage structuresofmailboxtypeinthecaseofsoftwaresystemsorbased ontheWishbonestandard[48]inthecaseofhardwaresystems.

Still regarding Fig. 3, it is possible to notice the presence of instances of agents in the clusters. Each agent, operating autonomouslyandcooperatively,isresponsiblefor:(i)acquisition ofsensoryinformation(relatedtothesensorysystem);(ii) encod-ing,storingandprocessinginformation;and(iii)actuationonthe environment.

Inordertoimplementsuchfunctions,eachagenthasa func-tionalstructureasillustratedinFig.4.Inthisfigure,onecansee thebasicelementsthatcomprisethecognitiveagent:thesensory system;thestorage,codingandprocessingsystem(SCPSystem); theAgentCoreand theAgentMemory.These elementswillbe describednext.

3.3. Sensorysystem

Thesensorysystemmustbeabletopickupstimuli(information) fromtheenvironmenttofeedtheSCPSystem.Themainfunction ofthisunitistoprocesscollectedinformationsuchthatitcanbe

Fig.4. Functionalstructureofeachagent.

Fig.5.Functionalstructureofthesensorysystem.

manipulatedbyotherfunctionalunitsofthesystemandtofacilitate theactuationontheexternalenvironment.Thesetwoprocessing typesrepresentthedeliberativeand reactivebehaviour, respec-tively[8].

Thesensorydeliberativesystemisresponsibleforinterfacing thesystemwiththeenvironmentforcapturing,forexample,image, sound or other waveforms (stream devices). Regardless of the devicetypeusedtocapturedata,thegoalofthesensory delibera-tivesystemconsistsinencodingexternalstimuliinconnectionist mappings.Theconnectionist mapswillbethecognitivesystem basis.

ThestructureofthesensorysystemispresentedinFig.5.As canbeseen,thesensorysystemintegratesmechanismsfor cap-turing both signals of the type stream and sampleddata from sensors/transducers.

Inthe fault prognosissystem,it is assumed thatthe Sensor DeviceInterfacereceivesinformationfromanon-linegas chro-matographunitthatprovidesthegasconcentrationsforH2,C2H4, C2H2,CH4,C2H6.

3.4. Storage,codingandprocessingsystem

Inordertoallowgreatermodularityandreuseofthestructures thatrepresentknowledge,theSCPSystemisbasedonahybrid topology,adopting,inthiscase,boththeconnectionistand sym-bolicmodels.

TheSCPinternalelementsformthebasisoftheimplemented knowledgebasedsystem,whichcanbereplicatedinclustersfor betterinformationrepresentation (classesofknowledge)andto providegreaterdynamismtoprocessing.Eachclustercanevoke functionsforthecreationorremovalofagentsorevenother clus-ters.ThesefunctionsareexportedbytheEmbryonicAgentLayer (factory),asshowninFig.3.

Forabetterdescriptionofthesystemmodelling,theinternal componentsoftheSCPSystemwillbesummarizednext.

3.4.1. Connectionistlayer

Theconnectionistlayerisrelatedtothereactivefunctionality, butalsoprovidingthebasisforthedeliberativefunctionality.The connectionistmappingsformthebasisofthecognitivesystem.

Fortheconnectionistlayer,weadoptanimplementationbased onensemblelearning,inwhicheachoperationstatusofthepower transformer(NormalCondition(NC),ElectricalFault(EF),Thermal Fault(TF)) isabstractedbya specificgroupofneuralnetworks. Inourimplementation,wehaveadoptedtheGeneralRegression NeuralNetwork(GRNN),originallyproposedin[49].

TheGRNNbelongstotheclassofprobabilisticneuralnetworks. Itsmain advantageisrequiring less trainingdata samplesthan

i=1exp(−(((x−xi) (x−xi))/(22)))

IntheGRNNmodel,xrepresentsaninput,thetrainingdata samplesarerepresentedby{xi;yi}.Whentheinputisclosetothe samplexj,theweightofthepredictionyjisclosetooneandthe con-tributionoftheothertrainingsamplesbecomessmaller.Withthis modelitispossibletopredictthebehaviourofsystemswithfew samplesandinterpolatesmoothlybetweentrainingdatasamples. Thesmoothnessparametercanbedeterminedwithaniterative method[49].

Ensemblelearningmethodsinvolveemployingafinitesetof differentclassifierstogetherinordertoachievebetterpredictive performance.Bootstrapaggregationwasadoptedfortheensemble classification[50].Inthisapproach,eachmodelintheensemble votewithequalweight,thustheoutputoftheensembleisthe sim-pleaverageoftheoutputofeachclassifier.Thereisoneensembleof Nneuralnetworkstrainedforrecognizingelectricalfault,another ensembleof Nneuralnetworks trainedfor recognizingthermal faultandanotherensemblefornormaloperation.Thesenetworks formtheconnectionistlayer.

ENC_(x)= 1 N N







ENC_{istheoutputoftheensembleassociatedwithnormaloperation,} ETF_{istheoutputoftheensembleassociatedwiththermalfaultand} EEF_{istheoutputoftheensembleassociatedwithelectricalfault.}

Intheprognosissystemforpowertransformers,consideringthe DissolvedGasAnalysis(DGA),theinputxiscomposedofthegas concentrationsforH2,C2H4,C2H2,CH4,C2H6andthelabelled out-putisequaltoonewhenbelongingtotheclass,andequaltozero whennotbelongingtotheclass.

3.4.2. SymbolicLayer

TheSymbolicLayerimplementsanevolutionarymechanismfor evolvingcandidateconditionsthatmaximizetheprobabilityofthe connectionistlayertodetectingfaults.Theevolutionaryalgorithm isbasedonEvolutionStrategies[51].Prognosisisadynamicprocess wherepredictionschangewithtimeandasnewobservationdatais available.Theintegrationoftheconnectionistandtheevolutionary modelisthebasisoftheprognosissystem.

Theinitialpopulationoftheevolutionarymechanismis gener-atedbasedonthecurrentsampleofgasconcentrationsfromthe sensorysystemanda statisticalmodelofthevariationofthese gaseswithtime.Thestatisticalmodel ofthedynamicsofthese concentrationsisdevelopedfromstudiesperformedby[52].The statisticalmodeltriestopredictthenextconfigurationofgases basedonthecurrentone.Therefore,giventhecurrentsampleof gasconcentrations,apopulationofindividualsisgeneratedwith thestatisticalmodel,providingpossiblefuturescenariosforthe currentconcentration.Thealgorithmisdetailedbelow.

1.GiventhecurrentsampleofgasconcentrationsfromDGA, gen-erateaninitialpopulation{pt,k},t=1,...,Tmax,k=1,...,,with

individualsbyapplyingthestatisticalmodelofthevariation ofthesegaseswithtime.

2.Classifytheindividuals{pt,k}withtheneuralnetworksinthe connectionistlayerintermsofnormaloperation,thermalfaultor electricalfault.Thefitnessvaluesoftheindividualscorrespond tothemaximumvalueamongtheoutputsoftheensemble net-works,whicharevaluesbetweenzeroandone.Theclassification isdeterminedbythebiggestoutput:

(pt,k)=max[ENC(pt,k),ETF(pt,k),EEF(pt,k)]

3.Generateoffspring{ot,j},j=1,...,byselectingone individ-ualwithbinarytournament and applyingrecombinationand Gaussianmutationtoitsvalues.Thebestparentshavehigher probabilityofbeingselectedforreproduction,henceproducing moreoffspring.

4.Classifytheindividuals,{ot,j},withtheneuralnetworksinthe connectionistlayerintermsofnormaloperationorfault.The fit-nessvaluesoftheindividualscorrespondtothemaximumvalue amongtheoutputsoftheensemblenetworks,whicharevalues betweenzeroandone.

5.ApplyES(+)selection,i.e.selectthebestindividualsbased onthefitnessvalues.Theseindividualswillformtheparent pop-ulationofthenextiteration.

6.Applythestatisticalmodelovertheparentindividuals,inorder topredicttheirpossiblenextstate.

7.Returntostep2untilconvergence.

Notice that the evolutionary model is actually evolving the populationinordertoassessfuturescenariosforthecurrent con-centrationofgases.Thosefuturescenariosthatarelikelytolead toarecognitionconditionareselectedandreproduced.Therefore, theprognosisisdonealongtheevolutionaryprocess.Themethod convergeswhentheentirepopulationisclassifiedasfaultorwhen themaximumnumberofiterationsisreached.

Eachiterationoftheevolutionaryalgorithmcorrespondstoone year,makingthebridgebetweenthemodelandtheprognosisof thepowertransformer.Whenthewholepopulationisclassifiedas fault,themethodstopsandthenumberofiterationsexecuted cor-respondstothenumberofyearsthatafault(electricalorthermal) ispredictedtooccurwith100%(inotherwords,theRUL). Other-wise,ifthemethodstopsbyreachingthemaximumnumberof iterations(Tmax)withouthavingtheentirepopulationclassifiedas fault,thentheprognosiscorrespondstoagivenconditionbasedon theaveragefitnessofthepopulationforeachensemble,whichgives aperspectiveoftheproportionofindividualsinthepopulationthat canbeclassifiedasfaultornormaloperation:

rNC(t)= _1 







TheprognosisisNormalCondition(NC)inthenumberofyears tcorrespondingtothecondition:

(rNC(t)>rTF_(t_))∧(rNC_(t_)>rEF_(t_))∧(rNC_(t_)>0.5)

TheprognosisisThermalFault(TF)inthenumberofyearst correspondingtothecondition:

(rTF(t)>rNC_(t_))∧(rTF_(t_)>rEF_(t_))∧(rTF_(t_)>0.5)

Fig.6. Prognosissteps.

TheprognosisisElectricalFault(EF)inthenumberofyearst correspondingtothecondition:

(rEF(t)>rNC_(t_))∧(rEF_(t_)>rTF_(t_))∧(rEF_(t_)>0.5)

3.5. OntologyCore

ThebasicfunctionoftheOntologyCoreistoexecutetheXML parser,whichperformssearchandmanipulationoperatorsoverthe XMLstructures(withXMLdenotingtheontologiesthemselves). Theontologies,inoursystem,providetherepresentationofthe knowledgeunder manipulation,e.g.,grammatical rules,a given knowledgespecificationoraninitialBayesiannetwork[53].The OntologyMemorymodulehasthefunctionofstoringontologies manipulatedbyeachagentcluster.

3.6. AgentCore

TheAgentCorehasthefunctionofperformingfunctions inher-enttotheagents.UnliketheOntologyMemory,theAgentMemory aimstostoreagentscodeandattributes(e.g.,theirstates). 3.7. ActuationSystem

Forinitialandprototypingpurposes,theActuationSystemis modelledasasimpletextoutputstream.

3.8. CommunicationSystem

In additiontotheneedtomodelmechanismsrelated tothe knowledge representation and the cognition environment, it is alsonecessarytohaveinfrastructureforcommunicationbetween agentsandbetweenagentclusters.Joiningthecognitive function-alities,whereeachclusterstorespartoftheknowledge,intraand intercommunicationmechanismsarealsorequired.InFig.3,these twocommunicationlevelsarerepresentedbyIntraClusterBusand InterClusterBusrespectively.Tofacilitatetheinitialprototype,we employamodelbasedonsharedbuffersofmailboxtype.

3.9. Faultdiagnosissystemontheproposedarchitecture

Tobetterillustratethepowertransformerfaultprognosis sys-temusingtheproposedarchitecture,Fig.6showsthesubsystems interactionsand theirfunctionalities.Thenumbersinthefigure indicatethesequenceofactionsduringthesystemutilization,as follows:

1.TheOntologyCore&MemoryprovidestotheConnectionistLayer, asdescribedinSection3.4.1,theneuralnetworksettingsandthe

1 26_{64 180}51 52₆₅ 530₇₇₀ 5₅ N_N 17/08/1984_10/10/1986 620 2200 2000 1100 13 T 05/08/1987 2 29 02 16 1 0.4 N 11/01/1983 27 5 12 2 0.4 N 13/02/1984 18 3 80 0.4 0.4 E 08/06/1989

op,operation;N,normal;T,thermalfault;E,electricalfault.

synapticweightsafterthetrainingstage.Itisimportantto

high-lightthatthetrainingofthemodelsintheConnectionistLayer

occursoff-lineusingdatasamplestakenbythepowercompany

ordatabasesavailablefromtheacademiccommunity.Thisdata

isusedtotraintheneuralnetworkinthetaskofrecognizing

normalandfaultconditionsfromthegasconcentrationvalues.

2.TheOntologyCore&MemoryprovidestotheSymbolicLayerthe

statisticalmodelofthegasesvariationwithtime.Thestatistical

modelcanbebuiltfromanalysescarriedoutinthespecialized

literatureor fromobservationsbuilt bythepowercompany.

Thus,theuseofthissystemdoesnotbecomeinfeasibleinthe

caseofutilitieswhichdonothavehistoricaldata.Additionally,

othermodelsforforecastingthegasesvariationcouldbeeasily

adoptedinthesystem.

3.SampledgasconcentrationsarepresentedtotheSensorySystem

toinitiatethefaultprognosis.

4.Theinformationofgasesconcentrations,afterdecodificationby

theSensorySystem,ispassedontotheSymbolicLayer,starting

theevolutionaryalgorithmdescribedinSection3.4.2.

5.ForeachiterationoftheevolutionaryalgorithminSection3.4.2, theConexionist Layeris triggeredtoperformtheanalysisand classification of the offspring generated by the evolutionary model.

6.Aftertheevolutionaryalgorithmstops,theprognosisreportis generated.

4. Results

4.1. Experimentaldata

Theneuralnetworksintheconnectionist layerweretrained withthedatasetIECTC10fromtheworkofDuvalascitedin[54]. Thereare73instancesofelectricalfault,34instancesofthermal faultand48instancesofnormaloperation.

ObtainingdatatotestandvalidatetheproposedPrognosis sys-temisnotaneasytaskbecauseutilitycompaniesdonotusually labelthemeasurementdataofpowertransformerswiththedateit wascollected.Nonetheless,weutilizeinformationavailablefrom [37],withdataprovidedbyanutilitycompanywhichperformed themonitoring of itstransformers, registering the gas concen-trationsandthedatesofcollectingthisdata.Twotestcasesare summarizedinTable2.Inthefirsttestcase,byanalysingthedata, thereisa thermalfaultafter4yearsand5months(4.42years). Inthesecondcase,thereisanelectricalfaultafter6yearsand5 months(6.42years).

4.2. Configurationofexperiments

Thefaultprognosissystemwasimplemented onaPC-based computerusingGNUC++compiler.Table3detailsthe characteris-ticsoftheenvironmentusedforimplementationandtesting.

Memory 3GBDDR3

Clockfrequency 2.53GHz

Operatingsystem CygWinemulatedonWindows7

CCompiler GNUC++vs4.5.3

Mathematicalanalysis Scilab5.4.1

4.3. Results

ThePrognosissystemwasappliedtothesetestcasesusingthe

concentrationdatacollectedonthefirstdateinTable2.Basedon

thisinitialsample,thePrognosissystemisstarted.Thesymbolic layergeneratesa populationwhose individualsareclassifiedas normaloperation,thermalfaultorelectricalfaultbythe connec-tionistlayer.Aftertheevolutionaryprocessconverges,weobtain thetypeoffaultandthepredictionofwhenitisexpectedtooccur. Sincethemethodisstochastic,werunourmethod120timesfor eachcase,generatingdifferentvaluesfortheRemainingUsefulLife (RUL)inyears.Theparametersusedintheexperimentare:=10, =100,N=5,Tmax=15.TheresultsaresummarizedinFig.7,which showstheboxplotoftheRULforalltheindependentrunsforeach testcase.

Aswecansee,thesystemcorrectlyconvergestoanestimate thatisonaverageveryclosetotheactualtimeofoccurrenceof thefault.ThepredictedRULforthecase1is4.75±1.18,whilethe predictedRULforthecase2is6.125±2.88.Figs.8and9showthe histogramsoftheresultsforeachcase.

Finally,weperformanadditionalexperiment,inwhichthe ini-tialstateisrandomlyselectedfromoneoftheentriesinTable2. Thenthemethodisappliedbasedonthisinitialstate,thusreaching aprognosis.TheobtainedRULiscomparedwiththeactualtimefor thefault,andtheerroriscalculated.After120runs,weobtained thefollowingresults:20%ofrunscorrespondedtotheexactfault time,33.33%ofrunscorrespondedtoanerrorequaltooneyear, 31.67%ofrunscorrespondedtoanerrorequal totwoyearsand 15%ofrunscorrespondedtoanerrorequalorgreaterthanthree years.Theseoutcomessuggestthattheproposedmethodhereis reliableforthisapplication.

Thecomputationalcostislow:oneindependentrunofthe pro-posedfault prognosissystemtakes onaverage6msin thetest environment,therefore,itisavailableassoonasasampleofgas concentrationisprovided.Ofcoursetheruntimedependsonthe

Fig.8.HistogramoftheRULforthecase1over120independentruns.

Fig.9.HistogramoftheRULforthecase2over120independentruns.

parametersoftheevolutionaryalgorithm,,andTmaxandonthe typeofmachinelearningtechniqueandnumberofneuralnetworks intheensembleintheconnectionistlayer.

Inordertogiveabetterideaabouttheaccuracyofthemethod, Table4presentstheconfusionmatrixofcase1andcase2.Itis consideredthatanacceptableprognosiserroriswithinoneyear(ı =1).

InTable4,consideringı=1and=5,letRTrepresentthereal faulttimeandPTrepresentthefaulttimepredictedbytheprognosis system.Wehave:

• TruePositive(TP):whenRT−ı≤PT≤RT+ı,thepredictedtimeis withinthemarginoferror.

• FalsePositive(FP):whenPT<RT−ı,therealfaultoccurredafter thetimeestimatedbytheprognosissystem.

• False Negative (FN): when RT+ı≤PT≤RT+, the real fault occurredbeforethetimeestimatedbytheprognosissystem. • True Negative(TN): when PT>RT+, thereal fault occurred

muchearlierthanthetimeestimatedbytheprognosissystem. Table4

Confusionmatrix.

Case TP FP FN TN

Case1 50.00% 2.50% 30.83% 16.67%

Case2 53.33% 45.00% 1.67% 0.00%

Fig.10.Accuracyoftheprognosiswithrespecttothetimebetweenthetimeofthe initialstateandthetimeoffailure.

FPimplyunnecessarypreventivemaintenanceactionwhileFN

representcorrectiveorreactivemaintenance,becauseafaultwas

notpredictedcorrectly.Althoughundesirable,FParelesscritical

thanFN.TNwouldrepresentasituationwherethesystemdoes

notpredictafaultandnofaultactuallyhappens.

FromtheconfusionmatrixdescribedinTable4,onecan

estab-lishtheaccuracyobtainedthroughtherelation ACC= TP_P+₊TN_N .

Fig.10showstheaccuracyinrelationtotheproximityoffaulttime. Wehaveobservedinourexperimentsthatwhentheinitialstateis closertowhenthefaultactuallyoccurred,theproposedsystemis moreaccurate.

5. Conclusions

Inthispaperweintroducedadata-drivenintelligentfault Prog-nosissystembasedoncognitivesystemsforhealthmonitoringin powertransformers.Theproposedsystemisimplementedona genericarchitectureformodellingcognitivesystems.Afault prog-nosis system providesmany benefits for the ElectricalUtilities becauseitcanassistinpowertransformerpreventivemaintenance, consequently,benefitingconsumerssuchashouseholds,industries andhospitals.

Thegenericarchitectureallows theimplementationof other knowledgebasedapplicationsandisflexibleenoughtoallowfor changesorimprovementsinthetechniquesemployedinthe sym-bolicandconnectionistlayers,ifrequiredfortheapplication.The proposedfaultPrognosissystemcombinesevolutionaryand con-nectionist mechanismsinto a hybridsystem.The connectionist layer employs ensemble learning for its potential in achieving higheraccuracyandrobustness.Ensemblelearningcancombine togetherdifferentclassifiersbasedonmachinelearningthatare availableintheliteratureand statisticalmethods.Thesymbolic layeremploysanevolutionarymethodandastatisticalmodelfor thedynamicsofthevariationofthegasconcentrations.

Theresultsreportedshowthatthesystemwasabletocorrectly predictnotonlythetypeoffaultbutalsowhenthatfaultisexpected tooccur.ThiskindofpredictionisveryusefulforSystemHealth Management.Thisisoneimportantcontributionofourwork,since itgoesonestepfurtherthanotherresearchintheliterature regard-ingfaultclassificationinpowertransformers.

Regardingcostsfordeploymentoftheproposedsystem,they couldbeestimatedasaminimumsincetheproposed methodol-ogycanbeimplementedasabasicembeddedsystem.Themost expensivepartisrelatedtotheDGAequipmentitself.

Sincetheproposedarchitectureisflexible,onecanuseitfor variousapplications.Giventhesecharacteristics,theapplicability ofthearchitectureforothercognitivesystemsapplicationsiswide. Moreoverthedevelopedfaultprognosisapplicationisofgeneral useinelectricpowersystems.

Asfuturework,weintendtoimprovetheaccuracyofthe con-nectionistlayerbytestingothermachinelearningmethodsinthe ensemble.Anotherimportantfollow-upofthisworkisthe hard-wareimplementation,leadingtoanelectronicsystemthatcouldbe actuallyembeddedinpowertransformers,thusintegratingsystem healthmonitoringandmanagementtothepowersystem equip-ment.

Acknowledgements

TheauthorswouldliketothanktheBrazilianagenciesCNPq (grant312276/2013-3)andFAPEMIG; andthesupportgivenby UFOP.

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