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Fast Fingerprints for Power System Events
David Bindel
6 Mar 2017
David Bindel SCAN 6 Mar 2017 1 / 1
The Computational Science & Engineering Picture
Application
Analysis Computation
MEMSSmart gridsNetworks
Linear algebraApproximation theorySymmetry + structure
HPC / cloudSimulatorsSolvers
David Bindel SCAN 6 Mar 2017 2 / 1
The Computational Science & Engineering Picture
Application
Analysis Computation
MEMSSmart gridsNetworks
Linear algebraApproximation theorySymmetry + structure
HPC / cloudSimulatorsSolvers
David Bindel SCAN 6 Mar 2017 3 / 1
Reminder: AC Power
Voltages V (t) =√
2Vavg cos(ωt)
Currents I (t) =√
2Iavg cos(ωt + φ)
Power Pavg = VavgIavg cosφ
David Bindel SCAN 6 Mar 2017 4 / 1
Biggest Machine(s) in the World
David Bindel SCAN 6 Mar 2017 5 / 1
SCADA: Supervisory Control and Data Acquisition
Non-synchronized measurements every 2–10 seconds
Report digital status and power flows
Complete observability in transmission grid
Voltages/currents inferred from power flows (state estimation)
Topology estimation co-evolved with state estimation
David Bindel SCAN 6 Mar 2017 6 / 1
Synchrophasors / Phasor Measurement Units (PMUs)
GPS-synchronized measurements and 10-30 reports per second
Directly report voltage and current angles/magnitudes
Really now coming into their own
First commercial PMU in 1992
Funding from American Recovery and Reinvestment Act of 2008
Now enough for partial observability in most places
We’re still figuring out what to do with them!
Model free system identification approaches (PMU only)
Dynamic state estimation / data fusion (PMUs + SCADA)
Today: topology update estimation from PMUs + SCADA
David Bindel SCAN 6 Mar 2017 7 / 1
Best of Both Worlds
Combine model-driven state estimates with PMU observations
Goal: Identify system (topology change) events(line outages, substation change, generator trips, ...)
Idea: Match PMU measurements E∆v to model predictions δvc
Need predictions for many possible changes c!
Each δvc depends on current state – constantly changing.
How can we do this fast?
David Bindel SCAN 6 Mar 2017 8 / 1
Power Flow Basics
Abstract power flow equation
H(v ;Y )− s = 0
In polar form: [H`
Hn+`
]=
n∑h=1
|v`||vh|[g`h b`h−b`h g`h
] [cos(θ`h)sin(θ`h)
],
with θ`h = θ` − θh and
s =[P1 · · · Pn Q1 · · · Qn
]T.
David Bindel SCAN 6 Mar 2017 9 / 1
Power Flows and PMUs
Power flow equationH(v ;Y )− s = 0
Measure Ev via PMUs (E ∈ {0, 1}m×n).
Suppose Ev shifts to Ev ′ = Ev + E∆v .
Goal: Map E∆v to change in topology (e.g. ∆Y ).
David Bindel SCAN 6 Mar 2017 10 / 1
Breaker, Breaker!
More detailed: breaker-level formulation
H(v ;Y ) + Cλ− s = 0
CT v = b
Equations in CT v = b have the form
vi − vj = 0 Equal voltage on bus sections
vi = bk Specified voltage at PV node
Can eliminate constraints if desired – but we don’t want to!
David Bindel SCAN 6 Mar 2017 11 / 1
Breaker, Breaker!
Breaker-level formulation
H(v ;Y ) + Cλ− s = 0
CT v = b
Notation:
x =
[vλ
], A =
[∂H∂v (v ;Y ) C
CT 0
], E =
[E 0
].
David Bindel SCAN 6 Mar 2017 12 / 1
Augmented Systems
Consider several possible topology changes:
Breaker closes in a substation (bus merge)
Breaker opens in a substation (bus split)
Load or generator trip
Line trip
Write each as an augmented power flow system.
David Bindel SCAN 6 Mar 2017 13 / 1
Example: Breaker Opening
Add a multiplier to “delete” previous constraint in C :
H(v ′;Y ) + Cλ′ − s = 0
CT v ′ + Fγ − b = 0
FTλ′ = 0
where F ∈ {0, 1}n×2 indicates voltage for “breakaway” segment.
David Bindel SCAN 6 Mar 2017 14 / 1
Example: Line Trip
After trip Y ′ = Y + ∆Y . H linear in Y , so:
H(v ′;Y ) + Uw + Cλ′ − s = 0
CT v ′ − b = 0
w − UTH(v ′; ∆Y ) = 0
where U ∈ {0, 1}n×4 indicates equations for end-point buses.1
1Can actually use three rather than four slack variables – see paper.David Bindel SCAN 6 Mar 2017 15 / 1
Strawman Method 0
For each possible topology update (and base case):
Compute predicted change ∆vpred
Compute mismatch µ = `(E∆vpred − E∆v)
Report (mismatch, update) pairs in descending order by mismatch.
Problem: Too many possible updates!
David Bindel SCAN 6 Mar 2017 16 / 1
Fast Fingerprints
Post-update systems linearized about pre-update state look like:[A UV T D
] [δxγ
]=
[d1d2
]For each possible update, estimate change to be Eδx .
Core matrix A does not change with updates – factor once
Border matrix and RHS do depend on updates
Solve bordered systems via a few solves with A
David Bindel SCAN 6 Mar 2017 17 / 1
Strawman Method 1
Save factorization of core matrix A
For each possible topology update (and base case):
Get linearized predicted change δvpred
Compute mismatch µ = `(Eδvpred − E∆v)
Report (mismatch, update) pairs in descending order by mismatch.
Problem: Too many possible updates! And maybe approximation error?
David Bindel SCAN 6 Mar 2017 18 / 1
Filtering
Care about some loss `(Eδx − E∆x), where[A UV T D
] [δxγ
]=
[d1d2
]Rewrite as
Eδv = EA−1(d1 − Uγ)
andU narrow and sparse =⇒(EA−1)U involves only a few columns of EA−1
David Bindel SCAN 6 Mar 2017 19 / 1
Partial Predictions
E∆v
µ m
EδvV
Find subspace Vc containing predictions Eδvc
Bound: subspace distance µ(c) ≤ mismatch m(c)
Sort events by ascending µ(c)
Check c1, . . . , ck until µ(ck+1) ≤ min1≤j≤k m(cj)
David Bindel SCAN 6 Mar 2017 20 / 1
Filter Effectiveness (IEEE 57-bus, line trips only)
0 10 20 30 40 50 60 70 80
Line number (sorted)
10−4
10−3
10−2
10−1
100
Approxim
ationerror
Blue squares are filter scores, red squares are actual mismatches.
David Bindel SCAN 6 Mar 2017 21 / 1
But is it right?
Test scenarios (IEEE 57-bus network)
Three PMU deployments: everywhere, sparse, and single
With no noise or Gaussian noise (σ = 0.0017)
Consistent with largest phase angle error allowed by the synchrophasorstandard (0.1 degree)
Will show behavior with bad data later
David Bindel SCAN 6 Mar 2017 22 / 1
Line Failure Diagnosis (sparse, no noise)
0 10 20 30 40 50 60 70 80
Line number (sorted)
10−5
10−4
10−3
10−2
10−1
100
Approxim
ationerror
Green/yellow: scores for correctly/incorrectly diagnosed tripped lines.Black crosses: scores for other lines.
David Bindel SCAN 6 Mar 2017 23 / 1
Example: Hard Case in IEEE 57-bus
2426
27
28
(24,26) tripped, (26,27) chosen. Color/thickness by mismatch−1/2.PMU locations are highlighted in blue.
David Bindel SCAN 6 Mar 2017 24 / 1
IEEE 57-bus: Line Failures
1 2 3 4 5 6 7 8 9 10
Rank
0.0
0.2
0.4
0.6
0.8
1.0
FractionofTests
All
Sparse
Single
1 2 3 4 5 6 7 8 9 10
Rank
0.0
0.2
0.4
0.6
0.8
1.0
FractionofTests
All
Sparse
Single
Single BF
David Bindel SCAN 6 Mar 2017 25 / 1
IEEE 57-bus: Substation Reconfigurations
1 2 3 4 5 6 7 8 9 10
Rank
0.0
0.2
0.4
0.6
0.8
1.0
FractionofTests
All
Sparse
Single
1 2 3 4 5 6 7 8 9 10
Rank
0.0
0.2
0.4
0.6
0.8
1.0
FractionofTests
All
Sparse
Single
Single BF
David Bindel SCAN 6 Mar 2017 26 / 1
IEEE 57-bus: Generator Trips
1 2 3 4 5 6 7 8 9 10
Rank
0.0
0.2
0.4
0.6
0.8
1.0
FractionofTests
All
Sparse
Single
1 2 3 4 5 6 7 8 9 10
Rank
0.0
0.2
0.4
0.6
0.8
1.0
FractionofTests
All
Sparse
Single
David Bindel SCAN 6 Mar 2017 27 / 1
IEEE 57-bus: Load Trips
1 2 3 4 5 6 7 8 9 10
Rank
0.0
0.2
0.4
0.6
0.8
1.0
FractionofTests
All
Sparse
Single
1 2 3 4 5 6 7 8 9 10
Rank
0.0
0.2
0.4
0.6
0.8
1.0
FractionofTests
All
Sparse
Single
Single BF
David Bindel SCAN 6 Mar 2017 28 / 1
IEEE 57-bus: All Contingencies
1 2 3 4 5 6 7 8 9 10
Rank
0.0
0.2
0.4
0.6
0.8
1.0
FractionofTests
All
Sparse
Single
1 2 3 4 5 6 7 8 9 10
Rank
0.0
0.2
0.4
0.6
0.8
1.0
FractionofTests
All
Sparse
Single
Single BF
David Bindel SCAN 6 Mar 2017 29 / 1
Bad Data Robustness via Huber
Experiments so far reflect `2 loss.
With outliers/bad data: use a more robust loss!
We use Huber with scale parameter 1.365 · 0.0017.
David Bindel SCAN 6 Mar 2017 30 / 1
IEEE 57-bus: All Contingencies
1 2 3 4 5 6 7 8 9 10
Rank
0.0
0.2
0.4
0.6
0.8
1.0
FractionofTests
L2 Loss
Huber Loss
One PMU delivers 5 degree errors (sparse PMU deployment) + Gaussiannoise on all readings.
David Bindel SCAN 6 Mar 2017 31 / 1
Ongoing Related Efforts
Spectroscopic event identification (Eric Lee, Nate Rogalskyj)
Goal: Identify state from ringdown/ambient oscillations
Approach: Residual + bound generation similar to FLiER
SECURED: Synthetic regulating reserves (Eaton, CMU, ANL, LLNL)
Goal: Reduce regulating reserve req’ts to offset VER
Approach: Fast distribution-level coordinated demand response
GridCloud (Birman, WSU)
Goal: Fast, reliable cloud infrastructure to communicate PMU data
Approach: Replication for performance and reliability
David Bindel SCAN 6 Mar 2017 32 / 1
Acknowledgements
FLiER: Practical Topology Update Detection Using Sparse PMUs.Ponce and Bindel, arXiv:1409.6644
David Bindel SCAN 6 Mar 2017 33 / 1
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