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:mike.zhou@interpss.org

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)

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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

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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)

• 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

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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

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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

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[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

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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(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.

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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)

– Massiveprocessingpower(750Blades,20Kcores)– Massivestorageroom(2.4PB,~2Mcases)– ProductionsupportforStateGriddispatchingcentersinChina

• Trainingdataset– Collectreal-worldsimulationcasesandresults– Basedonthehumanexperiencetogeneratemorescenariosbasedon

therecordedhistoryoperationcases– UsetotrainNN-modelsforthepredictiveanalysis

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CEPRIPowerSystemSimulationGroup

MLResearchRoadmap(2)• Simulationresultprocessing

– Thenewsimulationcenterwillgeneratemassivesimulationresult– Thehumanexpertsarenotcapabletoprocesstheresult– DigestmassivesimulationresultsusingNN-model– DiscoverknowledgetoguideChina’sUHVpowergridoperation

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Summary• AI,especiallyML,landscapehasbeenfundamentallychangedoverthelast5~10years– Thedevelopmentspeedisunprecedented– Manybreaking-throughsuccessfulstories

• Theenablingtechnologiesareaccessibletoeveryone– Powerfulcomputinghardware(CPU+GPU)– Newopensourcesoftwaretools

• Therighttimetorenew/restartresearchonapplicationofMLtopowergrid– Opencollaborationapproachisrecommended

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ThankYouQ&A

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