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Micro-SynchrophasorsforDistribu4onSystems
GRIDDATAMee4ngSanDiego,January27,2017
Dr.Alexandra(Sascha)vonMeier
Director,ElectricGridResearch,CaliforniaIns6tuteforEnergyandEnvironmentAdjunctProfessor,Dept.ofElectricalEngineeringandComputerScience
UniversityofCalifornia,Berkeley
SeanMurphyChiefScien6st
PingThings
Micro-synchrophasorsforDistribu4onSystems
Three-year,$4.4MARPA-EOPEN2012project(2013-2016)to• developanetworkofhigh-precisionphasormeasurementunits(µPMUs)
andhigh-speeddatabase(BTrDB)• exploreapplica6onsofµPMUdatafordistribu6onsystemstoimprove
opera6ons,increasereliability,andenableintegra6onofrenewablesandotherdistributedresources
• evaluatetherequirementsforµPMUdatatosupportspecificdiagnos6candcontrolapplica6ons
EXISTINGPMUNETWORKTransmissionSystem
μPMUNETWORKDistribu6onSystem
BTrDBserver
µPMU
µPMU
µPMU
µPMU
µPMU
(YourplaZormhere)
Micro-synchrophasornetworkconcept:Createvisibilityfordistribu4oncircuitsbehindthesubsta4ontosupportac4vemanagementandintegra4onofdistributedresources
ARPA-EµPMUProjectFieldinstalla4ons:
UCBerkeley/LBNLSouthernCaliforniaEdisonRiversidePublicU6li6es
AlabamaPower(SouthernCo.)GeorgiaPower(SouthernCo.)TennesseeValleyAuthorityPacificGas&ElectricCo.
Illustra4on:MeasuredphaseshiZalong12kVdistribu4oncircuit
voltagephaseangledifferencebetweenPVarrayandsubsta8on
currentinjectedbyPVarray
Usecaseexample:DiagnosecauseofDGunittrips
PVarraytrip
voltagesag
causedbyphaseB-Cfault(palmfrondcontact)downthefeeder
Tapchangeoccursover~2cyclesGraphshowsindividual120-Hzsamples
Tapchangeratsubsta8ontransformerstepsvoltageupanddownasloadchangesoverthecourseoftheday
Usecase:Detectnormalandmis-opera4onofequipment
Usecase:Detectnormalandmis-opera4onofequipment
Example:Anomalyintapchangesignaturegivesearlywarningoftransformeragingorincipientfailure
Curiousvoltagesagcharacteris8callyfollowstapchangeopera8on
Keyresults• µPMUinstrumentsmeetandexceedprojectgoals• Projecthasgathered100+TBofdistribu6ongriddataatunprecedentedfidelity
andresolu6on• BTrDB6me-seriesstorageandqueryprocessingperforms1400xfasterthan
leadingcommercialorresearchsolu6on,withunprecedentedstorageefficiency– plusuniqueaddi6onalfunc6onality:fastchangeset,sta6s6calsummary,consistent
versioningofdata&dataprocessing
• StrengthofµPMU-baseddiagnos6csderivesfromthe6medimensionanduniqueanaly6cframework:- measurementprecision,temporalresolu6on,precisesynchroniza6on(PMU)- dis6llateframework,exponen6altreesearchesatmul6ple6mescales(general)- interac6veanaly6cs&visualiza6on(general)
• Measurementshaveansweredcri6calearlyresearchques6onsaboutphasordataanddistribu6ongrids,whileraisingmanymore
• µPMUtechnologyhasmetwithsomeinterestintheu6lityindustry;addi6onalworkthroughtheresearchprojectwillhelpapplica6onsbedevelopedfurtherbytheprivatesector.
Micro-synchrophasorsforDistribu4onSystems,Part2
18-month,$2MPlus-Upextensionproject2017-2018Collabora6onwiththreecommercializa6onpartnerswithdifferentapplica6onfoci:
SmarterGridSolu6ons:Planning,diagnos8cs&mi8ga8onforhigh-penetra8onPVdistribu8on
DoosanGridTech(formerly1EnergySystems):Informa8oninfrastructurefordistribu8onmonitoringandcontrol
PingThings:StreamanalysissoMwareforreal-8megriddata,T&Ddisturbanceeventdetec8onandanalysis
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ProjectObjec4ves:• DeploySGSAc6veNetwork
ManagementDERMSplaZorminoneRPUtrialcircuitloca6on
• UseµPMUandotheru6litydatatoinformadvancedgirdautoma6onandcontrolapplica6ons
• EvaluatethebenefitsµPMUsbringtoplanningandreal-6mecontrolcomparedtotradi6onalgridmeasurements
“Planning,Diagnos8csandMi8ga8onforHigh-Penetra8onPVDistribu8on”
Partner:RiversidePublicU6li6es;TaskleadLBNL
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ValueProposi4on:• ManagegridswithhighDER
penetra6onsclosertotheirlimits,usingempiricaldataintegratedintothediagnos6c,planningandopera6onalprocess
• Defertheneedforgridreinforcementscausedbythegrowthofdistributedsolar
“Planning,Diagnos8csandMi8ga8onforHigh-Penetra8onPVDistribu8on”
“Informa8onInfrastructureforDistribu8onMonitoringandControl”Prospec6veu6litypartner:Aus6nEnergy;TaskleadCIEEProjectObjec4ves:• IntegrateµPMUdatawithDoosanGridTech’sIntelligentController
(DG-IC)• Buildalocalinforma6oninfrastructuretoenablemonitoringand
controlofcircuitperformanceonaDER-intensivecircuit,includingsolarPVandbaherystorage
• CreateadatabackbonetoenableDERcontrolschemesonafeederthatcanscaleacrossdevicetypes
“Informa8onInfrastructureforDistribu8onMonitoringandControl”ValueProposi4on:• Intelligentrecruitmentofdistributedresources,includingenergystorage
systems(ESS),maximizesthevalueoftheseassets• Extendslifeofotheru6lityassets,suchasvoltageregula6ondevices• Improvepowerqualityforthecustomer• AssurethatDERsa6sfieslocaldistribu6onconstraintswhileserving
needsofthetransmission6er
PingThings+µPMU“StreamAnalysisSoMwareforReal-TimeGridData”Prospec6veu6litypartner:PG&E;TaskleadCIEEProjectObjec4ves:• IntegrateµPMUdataintoStreamAnalysissoiware• Demonstrateapplica6onofStreamAnalysisacrosstransmissionand
distribu6onsystems• Studyspecificusecaseofgeomagne6cdisturbances(GMD)andtheir
impacts
PingThings+µPMU“StreamAnalysisSoMwareforReal-TimeGridData”
ValueProposi4on:• Informpreven6vemeasurestominimizecostlydamagesfromGMD
(es6mated8-10B$/yr)• Detec6onofanomaliessuchasvoltagesagssupportssitua6onal
awareness• Beherandfasterforensicanalysisofanomaliessaves6me,supports
mi6ga6onandul6matelypreven6on• Understandinganomaliesassociatedwithvariableanddistributed
genera6onsupportsappropriatemeasuresbyresponsibleparty,whetheru6lityorDGowner
Cross-cu^ngac4vi4esrelevanttoGRIDDATA
• FurtherdevelopmentonBerkeleyTreeDatabase
(BTrDB)• InterfacebetweenBTrDBandGridLAB-D• LBNLPowerDataportal
Other Services
Other Services
Other Services
LocalBuffer
LocalBuffer
LocalBuffer
LocalBuffer...
Chunk Loader
Field Datacenter / Cloud Clients
BTrDBCluster
Distillate Framework Cluster
PlottingService
HTTP
MATLAB
IPython
etc ...
JSON / CSV
Analytics with 3rd party tools
Interactive visualization
Wired
Event Detection Service
Other Services
webapp
PMU
PMU
PMU
PMU
Internet
LTE modem
BerkeleyTreeDatabase(BTrDB)
ARPA-Eresearchprojectconfigura6on:ca.40µPMUssending120HzdataviaEthernetor3G/4Gwireless,12streamsperdevice(voltageandcurrentmagnitude&phaseangle)
MichaelAndersen,UCBerkeley
BerkeleyTreeDatabase(BTrDB)resolvesthedownsidesofstoringandu4lizinglarge,high-resolu4on4me-seriesdatastreams• noneedtocompromisebetweendatacon6nuity,resolu6on,ease
ofaccess• extremelyfastsearches(~200msforindividualsampleswithin
monthsof120-Hzdata)• performsonlinecomputa6onofdatadis6llatestreams(e.g.power,
frequency,ratesofchange,differencesbetweenquan66es)• dataavailableforviewinginploheranddownloadablethroughAPI
forexternalanaly6capplica6ons• opensourcecodeavailableongithub
Transforma4veAdvancesinBTrDB
• Dis6lla6oninfrastructurewithextremelyfastchangesetiden6fica6on– Operatereal-6meonmanystreams,withholes,out-of-order,etc.
• On-the-Flysta6s6calsummariesoveramul6-resolu6onstore
• Mul6-resolu6onsearchandprocess– Find‘needle’eventsinimmensehaystacksinstantly– Drilldownexponen6allytoanalyze
BTrDB+GridLAB-D• GridLAB-Dplayerobjectsenableexternal6me-seriesdatato
beusedinsimula6ons• BTrDB+GridLAB-Dpythonscriptexposes6me-seriesdatain
GridLAB-Dplayerobjectformatinaspecialbuffer(namedpipeorFIFO)
• GridLAB-Dopensbufferasifitwasaregularplayerfile• Aseach6mestampisreadbyGridLAB-D,pythonscript
retrievesnext6mestampfromBTrDBandwritestothebuffer• Pythonscriptcanvary6mestampresolu6on
GridLAB-DModel
Valida6on
GridLAB-DWhat-ifscenariosgivesimulated
networkbehavior
Newdataanalysisandcontrolschemes
Real-worldnetwork
measurement
BTrDBSynchrophasorandTelemetry
data
BTrDB+GridLAB-D
ReadtheARPA-EProjectImpactSheetathhp://beci.berkeley.edu/wp-content/uploads/2016/12/UCB-External-Project-Impact-Sheet_11102016.pdfPeruseliveandarchivalµPMUdataathhp://plot.upmu.organdhhp://powerdata.lbl.gov/
LearnaboutµPMUhardwareathhp://www.powersensorsltd.com/PQube3.php
Par6cipateintheNASPIDistribu6onTaskTeam(DisTT)www.naspi.org
GostraighttothesourceforBTrDBathhps://github.com/SoiwareDefinedBuildings/btrdb
Resources
● An internet scale, real time stream analytics company anchored in data science
● Privately held with General Electric as one of several investors
● Member, active participant of IEEE, CIGRE, JSIS, NASPI…
● Currently working with utility clients in the Eastern Interconnect and WECC
● ARPA-E Grant participant working on µPMU analytics with LBNL, CIEE, UC Berkeley
Internet Scale, Real Time Stream Analytics
@pingthingsIO
Operational PMU Data Coverage
Tsunami
A cloud-based application that simulates global PMU data volume in real time.
Simulates
• thousands of PMUs and
• tens of thousands of data streams
Cloud-based, scalable
Cost effective to operate
Test framework for other products including data ingest engine
BTrDB Advancements
ISO NE
• October 2015 - July 2016
• All available PMUs
• 891 time series
• 577,368,000,000 data points
PG&E
• Core component to real-time data quality assessment tool
• Deployment in utility est. Feb 2017
Data Quality Assurance
• Real time data quality monitoring
• Monitors multiple aspects of data quality
• Web-based or off-line reporting
• Text/email real time alerts
• Designed to help identify location and root cause of the data problem with machine learning
Examining GMD Effects on Distribution