IEEE PES Technical Webinar Sponsored by IEEE PES Big Data ...
Transcript of IEEE PES Technical Webinar Sponsored by IEEE PES Big Data ...
ApplicationofMachineLearningtoPowerGrid Analysis
MikeZhou(StateGridEPRI,China)JianFeng Yan,DongYu Shi (ChinaEPRI,China)
Donghao Feng (KeDong ElectricPowerControlSysCom.,China)
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IEEE PES Technical Webinar Sponsored by IEEE PES Big Data Subcommittee
Contact Info:[email protected]
Agenda2
⢠Introduction⢠Open Platform for Applying Machine Learning (ML)⢠Power Grid Model Service⢠Research on Applying ML to Online DSA⢠ML Research Roadmap of CEPRI
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WhyMLResearchAgain?
⢠AlphaGoShowcaseâ âimpossibleforatleast10moreyearsâ⢠"ArtificialIntelligenceistheNewElectricityââ AndrewNg⢠Open-sourceMLtools(GoogleTensorFlow)
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1996
[1] âTensorFlow: An open-source software library for Machine Intelligenceâ, https://www.tensorflow.org/
[1]
BasicIdea4
[y] = [W][x] + [b][x] [y]
Layer(1) Layer(n)âŚ
Neural Network
MLApplicationAreas5
⢠ImageRecognition⢠SelfDrivingCar⢠Automation⢠Robotics⢠PredictiveAnalytics
â Powergridanalysishasbeenguidingtheoperationsuccessfully
â Powergridanalysissofarismodel-drivenâ Data-drivenMLapproachwillbesupplemental
Agenda6
⢠Introduction⢠Open Platform for Applying Machine Learning⢠Power Grid Model Service⢠Research on Applying ML to Online DSA⢠ML Research Roadmap of CEPRI
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NNModelTrainingData⢠MLMainSteps:1)Training;2)Prediction
â TrainingdataisthefoundationforML
⢠Trainingdatasetcollectionâ LargeuserdatasetcollectedbyGoogle,Facebook
⢠Trainingdatasetgenerationâ Powergridoperationdependsonthesimulation
⢠Guide thegridoperationwithprovenrecord⢠Contingencyanalysiscouldbedoneonly throughsimulation
â Needgridanalysistrainingdatagenerationtools/platforms
⢠OpenPlatformforApplicationofMLtoPowerGridAnalysishasbeencreated
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PlatformArchitecture8
Google ML Engine(TensorFlow)
PS Model Service(InterPSS)
Training CaseGenerator
(Pluggable)
1. Training
2. Prediction
SampleStudyCase
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⢠Load bus P,Q adjusted by a random factor [0~200%], load Q is further adjusted by random factor [+/-20%]
⢠The load changes are randomly distributed to the generator buses
Gen Area
Load Area
Training Case
IEEE-14 Bus case as the basecase. Power is flowing from the Gen Area to the Load Area. When the operation condition changes, predict⢠Bus voltage, P, Q⢠Interface flow⢠N-1 CA max branch power flow
NN-Model Prediction
Interface
BusVoltagePrediction(ACLoadflow)
⢠ACPowerFlowâ GivenbusPQ,computebusvoltage(mag,ang),suchthatmaxbus
powermismatch(dPmax,dQmax)<0.0001puâ 1000trainingdatasetsaregeneratedandusedtotraintheNN-model
⢠Input:busP,Q,P⢠Output:busvoltage,âŚ
⢠PredictionUsingNN-Modelâ 100testingcasesaregeneratedusingthesameprocessasthetraining
dataset.â ThetrainedNN-Modelisusedtopredictthebusvoltage
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dV(mag) dV(ang) dPmax dQmax
Maximum 0.00118pu 0.00229rad 0.00937pu 0.00619pu
Average 0.00028pu 0.00055rad 0.00225pu 0.00171pudV(msg,ang): Bus voltage predicted is compared with the accurate AC Power Flow results dP/Qmax: Bus voltage predicted is used to compute the network max bus power mismatch
Bus/InterfacePQPrediction(ACLoadflow)
⢠BusP,Qâ SwingBusP,Qprediction(100testingcases)
⢠Averagedifference: 0.00349pu 0.35MW/Var⢠Maxdifference: 0.01476pu 1.48MW/Var
â PVBusQprediction(100testingcases)
⢠Averagedifference: 0.00353pu 0.35MVar⢠Maxdifference: 0.02067pu 2.07Mvar
⢠InterfaceFlowâ Interfacebranchset[5->6,4->7,4->9]â InterfaceFlowP,Qprediction(100testingcases)
⢠Averagedifference: 0.00084pu 0.08MW/Var⢠Maxdifference: 0.00318pu 0.32MW/Var
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MaxBranchPowerFlowPrediction(N-1CA)
⢠N-1ContingencyAnalysis(CA)â InN-1CA,thebranchpowerflowiscalculatedwhenthereisabranch
outage.Furthermore,themaxbranchflowofeachbranchconsideringallcontingenciestochecklimitviolationorforscreening.
â 1000trainingdatasetsaregeneratedandusedtotraintheNN-model⢠Input:busP,Q,P⢠Output:maxbranchpowerflow
⢠PredictionUsingNN-Modelâ 100testingcasesaregeneratedusingthesameprocessasthetraining
dataset.â Maxbranchpowerflowpredictioniscomparedwiththeaccurate
simulationresults
⢠Averagedifference: 0.0134pu 1.34MW⢠Maxdifference: 0.0509pu 5.09MW
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OpenPlatformforApplicationofMLtoPowerGrid Analysis
⢠IntegrationofGoogleTensorFlowandInterPSSâ TensorFlowasMLengineâ InterPSS
⢠Providespowergridsimulationmodelservice⢠Pluggabletrainingdatagenerator
⢠ThePlatformhasbeenopen-sourcedâ Apache-2.0Licenseâ Open-sourceProjectLocationGitHub:https://github.com/interpss/DeepMachineLearning
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(Summary)
[2]
[2] âThe InterPSS Community Siteâ, www.interpss.org
Agenda14
⢠Introduction⢠Open Platform for Applying Machine Learning⢠Power Grid Model Service⢠Research on Applying ML to Online DSA⢠ML Research Roadmap of CEPRI
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PowerGridModelService15
⢠TheNeedForCreatingtheTrainingDataâ Powergridmeasurementdataisnotenoughâ Trainingdataforsecurityanalysisneedtobecreated
⢠N-1CA,transient/voltagestabilitylimit
⢠ValidNNModelPredictionAccuracyâ CommonMLApproach
⢠CollectedDataset=>Trainingset+Testingset
â Modelservicecreatesdataon-demandrandomlyoraccordingcertainrules
⢠BasedonInterPSSSimulationEngineâ Accuratepowergridsimulationmodelbehind
AboutInterPSS16
âSolving power grid simulation problem usingthe modern software approachâ
⢠InterPSS:InternetTechnology-basedPowerSystemSimulator
⢠InterPSSprojectstartedin2005â Object-oriented,Javaprogramminglanguageâ PSS/E,BPA,PSASP(ChinaEPRI)similarfunctionsâ Freesoftware
[3] M. Zhou, âSolving Power System Analysis Problems Using Modern Software Approach,â US Gov FERC Increasing Market and Planning Efficiency through Improved Software Meeting, DC June 2010.
[3]
InterPSSSoftwareArchitecture17
Application Suite
Traditional ApproachLittle could be extended
and customized
InterPSS Core Engine
InterPSS ApproachApplication created by
extension, integration and customization
Extensions
Desktop Edition
Cloud Edition
Integration with other systems
Ăź
[4] M. Zhou, Q.H. Huang, âInterPSS: A New Generation Power System Simulation Engine," submitted to PSCC 2018
[4]
PowerNetworkObjectModel18
[5] E. Zhou, "Object-oriented Programming C++ and Power System Simulation," IEEE Trans. on Power Systems, Vol. 11, No. 1 Feb. 1996.
[5]
AlgoA[ ]B[ ]C[ ]
X[ ]Y[ ]Z[ ]
Algo
ObjectModel
Process I/O In-Memory Data Exchange
Input Input
Output Output
Algorithm-Focused Pattern Model-Focused Pattern
⢠DataProcessingPatternsâ Algorithm-focused pattern
⢠Procedureprogrammingapproach⢠PSS/E, BPA,PSASP(China EPRI)basedonthispattern
â Model-focusedpattern⢠Object-orientedapproach
⢠InterPSSusestheModel-FocusedPattern
TrainingCaseGeneration19
Algo
InterPSSObject Model
Py4J
SimulationService
⢠ObjectandAlgorithmDecoupledRelationship⢠CommonAlgorithmImplemented
â TopologyAnalysis,Loadflow,N-1CA,StateEstimationâ ShortCircuitAnalysis,TransientStabilitySimulation
⢠TrainingDataGeneratorâ Trainingdatagenerationimplementedasaspecialalgorithmâ UsePy4Jastheruntimetohosttheobjectmodelandinterface
withTensorFlow(Python)
Google ML Engine(TensorFlow)
Process I/O
In-Memory Data Exchange
Training CaseGenerator
[6] âPy4J - A Bridge between Python and Javaâ, https://www.py4j.org/
[6]
Java
Python
PowerGridModelService20
⢠BasedonInterPSSSimulationEngine⢠ProvideFlexiblePowerGridModelService
â InterPSSpowernetworkmodelhostedinaJavaruntimeenvironment
â Pluggabletrainingdatagenerator⢠CreatecustomtrainingdatageneratorusingInterPSSpowernetworkobjectmodelAPI
(Summary)
Agenda21
⢠Introduction⢠Open Platform for Applying Machine Learning⢠Power Grid Model Service⢠Research on Applying ML to Online DSA⢠ML Research Roadmap of CEPRI F
DSAChallenges⢠CurrentDynamicSecurityAssessment(DSA)
â Repackageofoff-linesimulationprograms(TS,Small-signal)â Runninginthebatchmodeperiodically(15min)â InChinaStateGriddispatchingcenter,aroundtriptakes6-10minto
completeâ Theonlineanalysismodelsizeislarge-scale(40Kbuses)
⢠Challengesâ Thetime-domainsimulationhaslimitedspeed-uproomâ Thesimulationresultsarenotintuitivefortheoperatorsâ Remedyactionscannotbedirectlyderivedfromtheresults
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[7] M. Zhou, et al, âDevelopment of Fast Real-time Online Dynamic Security Assessment System,â IEEE SmartGrid NewsLetter, June 2016.
[7]
CCTPrediction⢠CriticalClearingTime(CCT)
â Maximumtimeduringwhichadisturbancecanbeappliedwithoutthesystemlosingitsstability.
â Determinethecharacteristicsofprotectionsâ Measurequantitativelysystemdynamicsecuritymargin
⢠CCTComputationâ ~100secusingthesimulationapproach(40KBus)â ML-basedapproach:usingNeuralNetwork(NN)modeltopredictCCT
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NN-ModelBasedCCTPrediction
⢠NN-Model(percontingency)isconstructed(trained)fortheCCTprediction;
⢠NN-Modelinput(FirstLayer Features):powergridmeasurementinfo,suchasGen(P,V);Substation(P,Q),andz(i,j)betweensubstations;
⢠AsetofLastLayer FeaturesarederivedandusedforCCTPrediction.
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First Layer Feature
Last LayerFeature
PredictionResult
CCT
PreliminaryResults25
NetworkSize
40K+Buses,3370Substations
NN-ModelOutput
CCTforaFault
FirstLayerFeatures
Gen(PăV)ďźSubstation(P, Q)ďźZbetweensubstationsďź
(Dimension :8772)
LastLayerFeatures
About 20
FeatureReduction
BasicNNunit: AutoEncoder
500kVĺçŤ
220kVĺçŤ
çĺ 500kVĺç˝
220kVĺç˝
AutoEncoder
AutoEncoder AutoEncoder...
...
CCT
AutoEncoder
éŤçş§çšĺž
CCTCalculation
Averageerror
Maxerror
Trainingcase
Testingcase
TimeNN-Model
TimeSimulation
AccRatio
AFault 2.65% 28.69% 24594 4660 2ms ~100s 1:50000
Last LayerFeature
First Layer Feature
i,j
BasicNNunit:AutoEncoder26
âThe aim of an AutoEncoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction.â
⢠About30mintrainingtime(oneGPU,40K-busnetwork)⢠NNModelInput(FirstLayerFeatures)
â GenP,V;SubstationP,Q;Zbetweensubstations (total8K+variables)â ThegoalisletAItoselectthrough trainingasetoflastlayerfeatures
(artificial)forpredictingCCT
⢠TheCurrentPracticeâ Asetofkeyfeatures(physical,suchasinterfaceflow)areselectedby
humanexperttomonitor thestabilityâ Usephysicalfeaturesorartificiallastlayer featurestodetermine
thesecuritymargin?
i,j
PotentialBenefit⢠Speed-upDSASystemResponseSpeed
â ForCCTprediction:50Ktimesfaster(40K-Bus,2msvs 100s)
⢠ProduceMoreIntuitiveResultsâ NNmodeltodigestlarge-scalesimulationoutcometocreatemoreintuitive
resultsâ Theâlookupâapproachisveryclosetohumanoperatorexperience
⢠EnhancedDecisionSupportâ NNmodelturns/reducesFirstLayer Features(P,Q,V)toLastLayerFeaturesâ UsetheLastLayer Featurestocomparethecurrentcasewithhistory
simulationcasestoidentifyâsimilarcasesââ Ifremedyactionsareneededforthecurrentcase,theycouldbefoundin
thesimilarhistorysimulationcases.
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Agenda28
⢠Introduction⢠Open Platform for Applying Machine Learning⢠Power Grid Model Service⢠Research on Applying ML to Online DSA⢠ML Research Roadmap of CEPRI F
CEPRIPowerSystemSimulationGroup
MLResearchRoadmap(1)⢠Newsupersimulationcenter(ChinaStateGrid)
â Massiveprocessingpower(750Blades,20Kcores)â Massivestorageroom(2.4PB,~2Mcases)â ProductionsupportforStateGriddispatchingcentersinChina
⢠Trainingdatasetâ Collectreal-worldsimulationcasesandresultsâ Basedonthehumanexperiencetogeneratemorescenariosbasedon
therecordedhistoryoperationcasesâ UsetotrainNN-modelsforthepredictiveanalysis
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CEPRIPowerSystemSimulationGroup
MLResearchRoadmap(2)⢠Simulationresultprocessing
â Thenewsimulationcenterwillgeneratemassivesimulationresultâ Thehumanexpertsarenotcapabletoprocesstheresultâ DigestmassivesimulationresultsusingNN-modelâ DiscoverknowledgetoguideChinaâsUHVpowergridoperation
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Summary⢠AI,especiallyML,landscapehasbeenfundamentallychangedoverthelast5~10yearsâ Thedevelopmentspeedisunprecedentedâ Manybreaking-throughsuccessfulstories
⢠Theenablingtechnologiesareaccessibletoeveryoneâ Powerfulcomputinghardware(CPU+GPU)â Newopensourcesoftwaretools
⢠Therighttimetorenew/restartresearchonapplicationofMLtopowergridâ Opencollaborationapproachisrecommended
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ThankYouQ&A
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