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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
2. Data volume
Berkeley Tree Database (BTrDB) essentially solves the problemwww.pingthings.ai http://btrdb.io/
easily zoom across time scales from sub-cycle to yearssingle platform for online and archival data analysis
R&D themes: minimizing cost to ingest & access each new data point; future: point-on-wave data?
2. Data volume
Berkeley Tree Database (BTrDB) essentially solves the problemwww.pingthings.ai http://btrdb.io/
easily zoom across time scales from sub-cycle to yearssingle platform for online and archival data analysis
R&D themes: minimizing cost to ingest & access each new data point; future: point-on-wave data?
individual sample identified in ~200 ms
Michael Andersen, UC Berkeley
3. Cost per unit
R&D themes:
• Device embedded PMUs
• 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
Hamed Mohsenian-Rad, UC Riverside
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
0 250 500 750 1000
Time (msec)
90
100
110
120
Cur
rent
(e)
0 250 500 750 1000
Time (msec)
285
286
287
288
289
Volta
ge
(f)
0 250 500 750 1000
Time (msec)
144
146
148
150
152
Phas
e
(g)
0 250 500 750 1000
Time (msec)
178
179
180
181
Phas
e
(h)
0 250 500 750 1000
Time (msec)
100
110
120
130
Cur
rent
(a)
0 250 500 750 1000
Time (msec)
7125
7135
7145
7155
7165
7175
Volta
ge
(b)
0 250 500 750 1000
Time (msec)
160
170
180
190
Phas
e
(c)
0 250 500 750 1000
Time (msec)
178
180
182
Phas
e
(d)
Hamed Mohsenian-Rad, UC Riverside
Example: Disturbance Event Location
Example: Asset Monitoring
https://www.naspi.org/sites/default/files/2018-11/03_mohsenian-rad_panel_20181024.pdf
0 0.5 1 1.5 2
Time (sec)
35
45
55
65
75
85
Cur
rent
(A)
A
B
C
0 0.5 1 1.5 2
Time (sec)
7190
7205
7220
7235
7250
7265
Volta
ge (V
)
0 0.5 1 1.5 2
Time (sec)
250
350
450
550
650
P (k
W)
0 0.5 1 1.5 2
Time (sec)
-150
-50
50
150
250
Q (k
VAR
)
UCR team diagnosed cap bank switching issue from nearby µPMU data
Hamed Mohsenian-Rad, UC Riverside
Example: Disaggregating DG from load
Ciaran Roberts and Emma Stewart, LBNLhttps://arxiv.org/pdf/1607.02919.pdf
-1000
0
1000
2000
3000
4000
5000
6000
7000
1 26 51 76 101
126
151
176
201
226
251
276
301
326
351
376
401
426
451
476
501
526
551
576
601
626
651
Estimate_Day1
Validate_Day1
-1000
0
1000
2000
3000
4000
5000
6000
7000
1 24 47 70 93 116
139
162
185
208
231
254
277
300
323
346
369
392
415
438
461
484
507
530
553
576
599
Estimate_Day3Validate_Day3
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.
Parts-per-billion measurements identify disturbance propagation
Grid Thumper voltage at PSL Alameda
voltage 80 km away from PSL
voltage 80 km away from PSL when notthumping
Active diagnostics with µPMUs: the Grid Thumper
https://www.powerstandards.com/product/grid-thumper/highlights/
Alex McEachern, Power Standards Lab
https://dst.lbl.gov/security/project/ceds-upmu/
https://www.energycentral.com/c/iu/holistic-approach-distribution-grid-intrusion-detection-systems
Compare independent intelligence from µPMU and SCADA data on state of distribution network and equipment operation
Use statistical and machine learning algorithms to:
• identify “normal”, ”abnormal”, or malicious operation
• determine if operation is “safe” or “unsafe”
• identify “reconnaissance” attacks
• identify and distinguish key classes of cyber attacks from equipment malfunctions or natural disasters.
LBNL Distribution Grid Security Project
Sean Peisert et al., LBNL
< 10 sensors per feeder flag discrepancies
EventDetect Infrastructure for Deep Learning
Jerry Schuman and Sean Murphy, PingThings; Michael Andersen, UC Berkeley; Emma Stewart, LLNL
Further Reading
https://ieeexplore.ieee.org/document/8340896
https://ieeexplore.ieee.org/document/7961200
https://www.naspi.org/node/688
