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ApplicationofMachineLearningtoPowerGrid Analysis
MikeZhou(StateGridEPRI,China)JianFeng Yan,DongYu Shi (ChinaEPRI,China)
Donghao Feng (KeDong ElectricPowerControlSysCom.,China)
1
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
F
WhyMLResearchAgain?
• AlphaGoShowcase– “impossibleforatleast10moreyears”• "ArtificialIntelligenceistheNewElectricity“– AndrewNg• Open-sourceMLtools(GoogleTensorFlow)
3
1996
[1] “TensorFlow: An open-source software library for Machine Intelligence”, https://www.tensorflow.org/
[1]
BasicIdea4
[y] = [W][x] + [b][x] [y]
Layer(1) Layer(n)…
Neural Network
MLApplicationAreas5
• ImageRecognition• SelfDrivingCar• Automation• Robotics• PredictiveAnalytics
– Powergridanalysishasbeenguidingtheoperationsuccessfully
– Powergridanalysissofarismodel-driven– 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
F
NNModelTrainingData• MLMainSteps:1)Training;2)Prediction
– TrainingdataisthefoundationforML
• Trainingdatasetcollection– LargeuserdatasetcollectedbyGoogle,Facebook
• Trainingdatasetgeneration– Powergridoperationdependsonthesimulation
• Guide thegridoperationwithprovenrecord• Contingencyanalysiscouldbedoneonly throughsimulation
– Needgridanalysistrainingdatagenerationtools/platforms
• OpenPlatformforApplicationofMLtoPowerGridAnalysishasbeencreated
7
PlatformArchitecture8
Google ML Engine(TensorFlow)
PS Model Service(InterPSS)
Training CaseGenerator
(Pluggable)
1. Training
2. Prediction
SampleStudyCase
9
• 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)
• ACPowerFlow– GivenbusPQ,computebusvoltage(mag,ang),suchthatmaxbus
powermismatch(dPmax,dQmax)<0.0001pu– 1000trainingdatasetsaregeneratedandusedtotraintheNN-model
• Input:busP,Q,P• Output:busvoltage,…
• PredictionUsingNN-Model– 100testingcasesaregeneratedusingthesameprocessasthetraining
dataset.– ThetrainedNN-Modelisusedtopredictthebusvoltage
10
2
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,Q– SwingBusP,Qprediction(100testingcases)
• Averagedifference: 0.00349pu 0.35MW/Var• Maxdifference: 0.01476pu 1.48MW/Var
– PVBusQprediction(100testingcases)
• Averagedifference: 0.00353pu 0.35MVar• Maxdifference: 0.02067pu 2.07Mvar
• InterfaceFlow– Interfacebranchset[5->6,4->7,4->9]– 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)– InN-1CA,thebranchpowerflowiscalculatedwhenthereisabranch
outage.Furthermore,themaxbranchflowofeachbranchconsideringallcontingenciestochecklimitviolationorforscreening.
– 1000trainingdatasetsaregeneratedandusedtotraintheNN-model• Input:busP,Q,P• Output:maxbranchpowerflow
• PredictionUsingNN-Model– 100testingcasesaregeneratedusingthesameprocessasthetraining
dataset.– Maxbranchpowerflowpredictioniscomparedwiththeaccurate
simulationresults
• Averagedifference: 0.0134pu 1.34MW• Maxdifference: 0.0509pu 5.09MW
12
2
OpenPlatformforApplicationofMLtoPowerGrid Analysis
• IntegrationofGoogleTensorFlowandInterPSS– TensorFlowasMLengine– InterPSS
• Providespowergridsimulationmodelservice• Pluggabletrainingdatagenerator
• ThePlatformhasbeenopen-sourced– Apache-2.0License– Open-sourceProjectLocationGitHub:https://github.com/interpss/DeepMachineLearning
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(Summary)
[2]
[2] “The InterPSS Community Site”, 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
F
PowerGridModelService15
• TheNeedForCreatingtheTrainingData– Powergridmeasurementdataisnotenough– Trainingdataforsecurityanalysisneedtobecreated
• N-1CA,transient/voltagestabilitylimit
• ValidNNModelPredictionAccuracy– CommonMLApproach
• CollectedDataset=>Trainingset+Testingset
– Modelservicecreatesdataon-demandrandomlyoraccordingcertainrules
• BasedonInterPSSSimulationEngine– Accuratepowergridsimulationmodelbehind
AboutInterPSS16
“Solving power grid simulation problem usingthe modern software approach”
• InterPSS:InternetTechnology-basedPowerSystemSimulator
• InterPSSprojectstartedin2005– Object-oriented,Javaprogramminglanguage– PSS/E,BPA,PSASP(ChinaEPRI)similarfunctions– Freesoftware
[3] M. Zhou, “Solving Power System Analysis Problems Using Modern Software Approach,“ 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
ü
[4] M. Zhou, Q.H. Huang, “InterPSS: 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
• DataProcessingPatterns– Algorithm-focused pattern
• Procedureprogrammingapproach• PSS/E, BPA,PSASP(China EPRI)basedonthispattern
– Model-focusedpattern• Object-orientedapproach
• InterPSSusestheModel-FocusedPattern
TrainingCaseGeneration19
Algo
InterPSSObject Model
Py4J
SimulationService
• ObjectandAlgorithmDecoupledRelationship• CommonAlgorithmImplemented
– TopologyAnalysis,Loadflow,N-1CA,StateEstimation– ShortCircuitAnalysis,TransientStabilitySimulation
• TrainingDataGenerator– Trainingdatagenerationimplementedasaspecialalgorithm– UsePy4Jastheruntimetohosttheobjectmodelandinterface
withTensorFlow(Python)
Google ML Engine(TensorFlow)
Process I/O
In-Memory Data Exchange
Training CaseGenerator
[6] “Py4J - A Bridge between Python and Java”, https://www.py4j.org/
[6]
Java
Python
PowerGridModelService20
• BasedonInterPSSSimulationEngine• ProvideFlexiblePowerGridModelService
– InterPSSpowernetworkmodelhostedinaJavaruntimeenvironment
– 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)
– Repackageofoff-linesimulationprograms(TS,Small-signal)– Runninginthebatchmodeperiodically(15min)– InChinaStateGriddispatchingcenter,aroundtriptakes6-10minto
complete– Theonlineanalysismodelsizeislarge-scale(40Kbuses)
• Challenges– Thetime-domainsimulationhaslimitedspeed-uproom– Thesimulationresultsarenotintuitivefortheoperators– Remedyactionscannotbedirectlyderivedfromtheresults
22
[7] M. Zhou, et al, “Development of Fast Real-time Online Dynamic Security Assessment System,” IEEE SmartGrid NewsLetter, June 2016.
[7]
CCTPrediction• CriticalClearingTime(CCT)
– Maximumtimeduringwhichadisturbancecanbeappliedwithoutthesystemlosingitsstability.
– Determinethecharacteristicsofprotections– Measurequantitativelysystemdynamicsecuritymargin
• CCTComputation– ~100secusingthesimulationapproach(40KBus)– ML-basedapproach:usingNeuralNetwork(NN)modeltopredictCCT
23
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.
24
First Layer Feature
Last LayerFeature
PredictionResult
CCT
PreliminaryResults25
NetworkSize
40K+Buses,3370Substations
NN-ModelOutput
CCTforaFault
FirstLayerFeatures
Gen(P、V);Substation(P, Q);Zbetweensubstations;
(Dimension :8772)
LastLayerFeatures
About 20
FeatureReduction
BasicNNunit: AutoEncoder
500kV厂站
220kV厂站
省内500kV子网
220kV子网
AutoEncoder
AutoEncoder AutoEncoder...
...
CCT
AutoEncoder
高级特征
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
“The aim of an AutoEncoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction.”
• About30mintrainingtime(oneGPU,40K-busnetwork)• NNModelInput(FirstLayerFeatures)
– GenP,V;SubstationP,Q;Zbetweensubstations (total8K+variables)– ThegoalisletAItoselectthrough trainingasetoflastlayerfeatures
(artificial)forpredictingCCT
• TheCurrentPractice– Asetofkeyfeatures(physical,suchasinterfaceflow)areselectedby
humanexperttomonitor thestability– Usephysicalfeaturesorartificiallastlayer featurestodetermine
thesecuritymargin?
i,j
PotentialBenefit• Speed-upDSASystemResponseSpeed
– ForCCTprediction:50Ktimesfaster(40K-Bus,2msvs 100s)
• ProduceMoreIntuitiveResults– NNmodeltodigestlarge-scalesimulationoutcometocreatemoreintuitive
results– The“lookup”approachisveryclosetohumanoperatorexperience
• EnhancedDecisionSupport– NNmodelturns/reducesFirstLayer Features(P,Q,V)toLastLayerFeatures– UsetheLastLayer Featurestocomparethecurrentcasewithhistory
simulationcasestoidentify“similarcases”– Ifremedyactionsareneededforthecurrentcase,theycouldbefoundin
thesimilarhistorysimulationcases.
27
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)
– Massiveprocessingpower(750Blades,20Kcores)– Massivestorageroom(2.4PB,~2Mcases)– ProductionsupportforStateGriddispatchingcentersinChina
• Trainingdataset– Collectreal-worldsimulationcasesandresults– Basedonthehumanexperiencetogeneratemorescenariosbasedon
therecordedhistoryoperationcases– UsetotrainNN-modelsforthepredictiveanalysis
29
CEPRIPowerSystemSimulationGroup
MLResearchRoadmap(2)• Simulationresultprocessing
– Thenewsimulationcenterwillgeneratemassivesimulationresult– Thehumanexpertsarenotcapabletoprocesstheresult– DigestmassivesimulationresultsusingNN-model– DiscoverknowledgetoguideChina’sUHVpowergridoperation
30
Summary• AI,especiallyML,landscapehasbeenfundamentallychangedoverthelast5~10years– Thedevelopmentspeedisunprecedented– Manybreaking-throughsuccessfulstories
• Theenablingtechnologiesareaccessibletoeveryone– Powerfulcomputinghardware(CPU+GPU)– Newopensourcesoftwaretools
• Therighttimetorenew/restartresearchonapplicationofMLtopowergrid– Opencollaborationapproachisrecommended
31
ThankYouQ&A
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