Innovative PMU Applications for Distribution Systems WECC JSIS Meeting in Portland, OR November 8-9, 2018 Alexandra “Sascha” von Meier Adjunct Professor, Dept. of Electrical Engineering and Computer Science Director, Electric Grid Research, California Institute for Energy and Environment University of California, Berkeley
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Innovative PMU Applications for Distribution Systems
WECC JSIS Meeting in Portland, OR November 8-9, 2018
Alexandra “Sascha” von MeierAdjunct Professor, Dept. of Electrical Engineering and Computer ScienceDirector, Electric Grid Research, California Institute for Energy and EnvironmentUniversity of California, Berkeley
Phasor measurements exist mainly at the transmission level today, for three reasons:
1. Distribution systems require more precise measurements
2. No need to monitor traditional, passive distribution systems
3. Cost-value proposition
Divisions are blurring:
Prosumers | Utility grid Transmission | Distribution
Meters and substations still denote boundariesbut power system analytics must cross over
1. Measurement precision & accuracy
2. Data volume
3. Cost per unit
Challenges for Distribution PMUs, answered
1. Measurement precision & accuracy
voltage phase angle difference between PV array and substation
current injected by PV array
Very small phase angle differences < 1o along distribution circuits
1. Measurement precision & accuracy
µPMU resolves 10 mdeg of phase angle, 0.01% TVEhttps://www.powerstandards.com/product/micropmu/highlights/
Systematic errors from transducers (PT, CT) compromise accuracy of absolute measurements
R&D themes: online transducer calibration;algorithms based on time-series (relative) measurements
• Algorithms that derive more intelligence from fewer sensors, of diverse quality
• Architecture: many applications on one sensor network drive a holistic business caseNote: this applies across distribution & transmission!
• Event monitoring and analysisFault detection, fault location
• Asset monitoringEquipment health diagnostics (tap changers, capacitor banks)
• Topology detectionBreaker and switch status, islanding, restoration
• Model validationPhase ID, feeder hosting capacity, impedance estimation
• DG CharacterizationFeeder impacts of variable solar generation
Distribution PMU applications of local interest
• Event monitoring and analysisSupport wide-area diagnostics from behind the substation
• Characterizing Distributed GenerationDG-Load disaggregation to estimate actual generationbehind the meter, masked load; Diagnosing inverter trip behaviorUnderstand system exposure to loss of generation; provide intelligence for safe system restoration
• CybersecurityCyberattack detection through redundant monitoring
• Control ApplicationsPotential for new control strategies to promote grid resilience
Distribution PMU applications of system-wide interest
Example: Event monitoring at the distribution feeder level
6 second delay before step change in SCADA
event captured only by µPMUs
current step up after transient
Emma Stewart and Ciaran Roberts, Lawrence Berkeley National Lab
µPMUSCADA
High-resolution, time synchronized measurements vastly outperform SCADA
Example: Distribution Asset Monitoring
Example: Distribution Asset Monitoring
Emma Stewart et al., LBNL
Example: High-impedance fault detectionVo
ltage
pha
se a
ngle
(deg
)
Emma Stewart et al., LBNL
Example: Diagnosing cause of inverter trips
PV array trip
voltage sag
Emma Stewart et al., LBNL
Example: Disturbance Event Location
Y1
Z1 Zn-1
∆Ik
∆Vn∆Vk
|Vd| Vd
|Id| IdZu Zd
∆V2 ∆Vn-1∆V1
∆I1 Y2
∆I2Yk
∆IkYn-1
∆In-1Yn
∆In
Downstream
|Vu| Vu
|Iu| Iu
UpstreamZk-1∆Vk-1
∆Ik-1
Zk∆Vk+1
∆Ik+1
UC Riverside algorithm:forward and backward voltage nodal calculation, using pre- and post-event voltage and current phasors, to estimate location of current sourceon radial feeder
Note: this application requires both rms magnitude and phase angle measurements
Ciaran Roberts and Emma Stewart, LBNLhttps://arxiv.org/pdf/1607.02919.pdf
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LBNL algorithm estimates PV generation as a function of PV capacity, nearby irradiance data and aggregate power measurement (µPMU 1).Model runs in real time to approximate actual PV output and identify masked load. Experimental validation with µPMU 2.R+D 100 Award 2017, Patent awarded.