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Spoofing State Estimation William Niemira

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Overview State Estimation DC Estimator Bad Data Malicious Data Examples Mitigation Strategies 2

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State Estimation Finite transmission capacity Economic and security aspects must be managed – Contingency analysis – Pricing – Congestion management Accurate state information needed 3

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State Estimation Networks are large –Thousands or tens of thousands of buses –Large geographical area Many measurements to reconcile –Different types –Redundant –Subject to error 4

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State Estimation State estimation uses measurement redundancy to improve accuracy Finds best fit for data Differences between measures and estimates can indicate errors 5

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DC Estimator Overdetermined system of linear equations Solved as weighted-least squares problem Assumes: –Lossless branches (neglects resistance and shunt impedances) –Flat voltage profile (same magnitude at each bus) Reduces computational burden 6

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DC Estimator 7

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1-Bus Example 10

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1-Bus Example 11

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1-Bus Example 12

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3-Bus Example – 50 θ 2 – 100 θ 3 = P 1 meas 150 θ 2 – 100 θ 3 = P 2 meas – 100 θ θ 3 = P 3 meas – 100 θ 3 = P 13 meas 13

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3-Bus Example 14

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3-Bus Example 15

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Bad Data Bad data usually consists of isolated, random errors These types of errors tend to increase the residual Measurements with large residuals can be omitted to check for better fit Works well for non-interacting bad measurements 16

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1-Bus Example Good Data Bad Data 17

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Malicious Data 18

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Attack Formation 19

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1-Bus Example 20

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1-Bus Example 21

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1-Bus Example Unattacked Attacked 22

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For Real? In practice, state estimators are more complicated than previous examples Assumed strong adversary: –Has access to topology information –Has some means to change measurements Why would someone do this? –Simulate congestioncould affect markets –Reduce awareness of system operator 23

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AC Estimator AC model accounts for some effects neglected in the DC model Attacks as generated earlier will affect residual Attack may not have effect intended by adversary 24

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AC Estimator 25

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AC Estimator 26

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AC Estimator DC approximation is pretty good Harder to detect attack than random error Relatively large attacks may escape detection Grid state affects quality of DC attack 27

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Detection Focus on quantities neglected by DC model (VARs) VARs tend to be localized Attack Losses change VAR flow and generation changes 28

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Detection Alternative approach is to estimate parameters simultaneously with states Augment state vector with known parameters Compare known values to parameter estimates to find bad data 29

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Detection Choose something known to the control center but not an attacker Example: TCUL xformer tap position, D- FACTS setting Attacks will perturb parameter estimates 30

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Conclusions State estimators, even nonlinear estimators, are vulnerable to malicious data Malicious data is different from conventional bad data Nonlinearity effects of the attack may be detectable Parameter estimation can verify data 31

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Questions? Thank you! 32

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