Enhancing power state estimation accuracy and cyber-security in the

smart grid

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Yacine Chakhchoukhychakhch@asu.edu

ECE 421/2017

Institutions

• Arizona State University:

• Salt River Project power and water

• Tokyo Institute of Technology:

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Outline

• Smart grid: cyber-physical system

• Power state estimation for the smart grid

• Cyber-security for the smart grid: application to the state estimation

• Future research directions

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Smart grid: cyber-physical system

NISTR: National Institute of Standards and Technology Report

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Smart grid: trends and challenges

• Increased renewable and distributed generation (solar andwind), storage and load management.

• More competitive and free electricity markets and loadsresponding to the price (smart meters).

• Increased communication and integration of IT (informationtechnologies) and ICT (information and communicationtechnologies), sensed data (smart meters) and need forcontrol.

The grid should operate efficiently, reliably, securely.Confidentiality should be insured as well.

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Power state estimation for the smart grid

• State estimation (SE) evaluates the bus voltage magnitudes andphase angles exploiting measurements communicated to thecontrol center with SCADA (Supervisory Control and DataAcquisition).

• SE is useful for real time operation, i.e., contingency analysis,control, in power markets.

• Static SE typically occurs in 30-s intervals (AZ, USA)

⇒ possibly more frequently in future.

• The financial and real life consequences of bad SE can betremendous (2003 blackout) ⇒ Important research topic.

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Power State estimation example

• State estimation (SE) evaluates the bus voltage magnitudes and phase angles using redundant power injection measurements, line power flows at different locations, voltage magnitudes.

V2? d2?

V5? d5?

Flow measure

Injectionmeasure

IEEE 14 bus system

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Static state estimation in the smart grid

The practical iterative algorithm:

Estimate the vector x from the measurements vector z

is linked to the topology of the grid

H is the Jacobian of h(.), i.e.,

Stop the algorithm if:

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𝒛 = 𝒉 𝒙 + 𝒆

𝒙 : voltage magnitudes and phase angle,

: power flows, power injections and voltage magnitudes,𝒛

: gaussian noise (null mean and covariance R),𝒆

𝒉(. )

𝒙(𝒌+𝟏) − 𝒙 𝒌 ≤ 𝝐

𝐻 = 𝜕𝒉(. ) 𝜕 𝒙

(weighted least squares)

𝒙 𝑘+1 = 𝒙 𝒌 + 𝐻𝑇𝑅−1𝐻 −1𝐻𝑇𝑅−1(𝒛 − 𝒉( 𝒙 𝒌 )

Bad data detection in state estimation

Analyze the residual vector after convergence (Gaussian?):

Reject all observations outside the 99.7% confidence interval as outliers.

- Check the normalized residuals (RA)

- The c2- test is used

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𝒓 = 𝒛 − 𝒉( 𝒙 𝒌

Hybrid state estimation in the smart grid

• The phasor measurement units (PMUs) measure thevoltage magnitudes and phase angles directly.

• The measurements from PMUs are synchronized thanks tothe use of GPS.

• Different reporting rates of conventional measurements (anew measurement every 2-5 s) and PMU measurements(30-120 measurements per second).

• PMUs are costly and still limited i.e. SE combines bothregular measurements (SCADAs) and PMUs (Hybrid SE).

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PMU measurements characteristics

• The PMUs’ clocks are synchronized using the 1 ps (persecond) signal from the GPS. Errors are present in thesubintervals.

• Faulty synchronization of PMUs is called by time skew.

0 1 2 3 4 5 6 7 8 9 10287

288

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296

t[s]

Vo

lta

ge

An

gle

[De

gre

e]

Jumps are present (every second) in the PMU phase angle measurements

Solution are proposed, for example: [Zhang et all. 2012]

Jumps

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PMU measurements characteristics

• If the PMU noise is Gaussian, the averaging the PMU recordedmeasurements would reduce the noise in the static case .

Nbl

Resi

du

al

Uncertainty of data

Variation of data

Buffer length

Esti

mat

ion

Err

or

Objective: insure the best possible tradeoff betweenreducing noise uncertainty and tracking system changes

Solution: A simple hypothesis testing based method is proposed in [Zhang et al. 2013]

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Application on a real life system

Evaluation at five different load conditions

Set 1

Set 2

Set 3

Set 4

- Part of the Western Electricity Coordinating Council system (WECC): 1310buses, 1820 branches, 200 generators and 5000 SCADA measurements

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Application on a real life system

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Set 1 (88) Set 2 (89) Set 3 (89) Set 4 (89) Set 5 (90)

2 -

No

rm

Dataset number ( Buses count)

Active power injection residuals - Level 1

No BL

BL - First

BL - Second

No BL – Case without any PMU measurementsBL First – Case with algorithm from project 1 BL Second – Case with algorithm from project 2

– Level 1: PMU buses and busesdirectly connected to them

[Murugessen et al. 2015] 14

Application on a real life system

0

0.5

1

1.5

2

2.5

Set 1 (84) Set 2 (84) Set 3 (85) Set 4 (86) Set 5 (86)

2 -

No

rm

Dataset number (Buses count)

Reactive power injection residuals - Level 1

No BL

BL - First

BL - Second

The improvement wasobtained for up to level 3.

[Murugessen et al. 2015]

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Modeling PMUs correlation in SE

• Measured PMUs show both time correlation and spacecorrelation Dependence between

adjacent PMUs

Dependence in time at a single PMU 16

Modeling PMUs correlation in SE

Weighted Least squares

Buffer recorded PMUs

Vectorial Autoregressive models (VAR(p)):

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𝒀𝑡 = 𝜑1 𝒀𝑡−1 +⋯+ 𝜑𝑝 𝒀𝑡−𝑝 + 𝜺𝒕,

(𝒀𝑡 ∈ ℝ𝑘

𝒀𝑡

𝑧1⋮𝑧𝑚

Modeling PMUs correlation in SE• Considering multiple short-order, small dimensional

Vectorial Autoregressive models (VARs) improves theperformance while being tractable.

- A1 last arriving PMU.- A2 averaging the data (buffer).- A4 space-time correlation.

Monte Carlo average absolute errorin voltage phase angle

PMUs

[chakhchoukh et al. 2014]

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IEEE 118 bus system

Cyber-security for the smart grid

• More information and communication technologies (ICT) inthe cyber-part of the smart grid.

• Increased connection and access points from externalnetworks.

• Large amount of data available from new sensors

Increased cyber-vulnerabilities with huge impacts(financial and operation security, reliability )

Investigating and improving the Cyber-securityis necessary (R&D, standards, regulations)

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Cyber-security for the smart grid

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• Secure the network communication: firewalls, anti-virus, secure authentication,…

• System-based cyber-security: offers defense against legal communication malicious attacks.

System-based solutions offer detection against attacks that pass conventional ICT defenses

Cyber-security for power state estimation

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Linear state estimation:

If covariance(e)=I, the least squares estimator is

If the communicated measurements are modified by attacker

The new state is:

The vector of residuals is:

(Changed state!)

(Residuals are kept the same! Stealthy attack!)

[Liu et al. 2009]

𝒛 = 𝐻 𝒙 + 𝒆

𝒙 = 𝐻𝑇𝐻 −1𝐻𝑇𝒛

Cyber-security for power state estimation

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Attacks on the measurementsAttacks on the Jacobian

• Consider attacks on the topology of the grid:

Is it possible to have stealthy attacks?

• H is the Jacobian matrix • The topology is updated over time (states of circuit-breakers, line

parameters)• In the literature, random errors are reported on H, they generate

leverage points

𝐻𝑐 = 𝐻 + 𝛿𝐻

Cyber-security for power state estimation: bad leverage points impact

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Bad leverage point

True estimate

Least squaresestimate

Outlier

Smallresidual

Bias

Cyber-security for power state estimation

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- Theorem: If the attack satisfies

A possible solution is to use the least trimmed squares (LTS) estimator

: trimming fraction

, then the attack is stealthy.

- Distributed algorithm: maximum robustness and fast execution

- Masked attacks where the attacker does not control the state.(Residuals changed but attacks difficult to identify)

- Stealthy attacks where the attacker controls the state. (Residuals kept the same, Both topology and state are changed )

[chakhchoukh et al. 2015]

Cyber-security for power state estimation

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Voltage phase angle estimate at bus 6 in the IEEE 14 bus system

Existing and proposed Estimators (Attacker’s target: -10.4 deg)

True state:-15.66 deg

Estimate with no-attack

Both least trimmed squares and classicalrejection (RA) are erroneous

Monte Carlo average absolute error of SE in the IEEE 30 bus system

Possible solutions against cyber-attacks

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LTS1 (a1)

LTS2 (a2)

LTSn (an)

Decision rules

Compares the multiple estimates

SCA

DA

mea

sure

men

ts,

Top

olo

gy

Attacker creates masked attacks

Data collected from the grid

Estimation module

Detects attacksRobust state

Solution 2:

Solution 1:

Store an erroneous topology that misleads the attacker, (The operator creates a topology that maximizes detection)

[Chakhchoukh et al. 2016]

Several trimming fractions

[Chakhchoukh et al. 2015]

Possible solutions against cyber-attacks

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time

Stealthy Attack

- Learn a model considering historical data which is clean, machine learning techniques could be used (density ratio estimation).[Chakhchoukh et al. 2016]

Stored clean data

Density ratio module gives an alarm to

the operator

Corrupted state or topology

Solution 3:

Robust hybrid state estimation

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Time series context:Estimate Robustly VAR models

Regression Context: Robust estimation for SCADA measurements and topology errors

[Chakhchoukh et al. submitted]

Future research

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State estimation for the smart grid:

• Hybrid state estimation with the inclusion of PMUs, dynamic power SE and distributed SE.

• Distribution power systems: three-phase SE.

• Remedial actions and counter-measures: machine learning, robust theory, securing sensors.

Distributed control considering renewable integration and cyber-threats:

• Automatic generation control (AGC)

• Power flow control, voltage regulation and control, damping inter-area oscillation modes.

Research the cyber-security for the smart grid

Future research

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• Considering the integration of more renewable energy on the grid:forecasting solar and wind generation, stochastic power flow andassess the reliability of the grid.

• Exploit and develop co-simulators and test beds (includingcommunication) for power systems to assess the cyber-security onsmall and large power systems.

• Consider real life data and cases to evaluate the proposed methods.

The impact of the research can be general as well, for example, the tools could be used for different applications.

Conclusions

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• Cyber-security for the power systems is becoming crucial and furtherinvestigation is needed.

• New exciting challenges raised by the integration of renewableenergy generation (solar and wind) and new sensors (PMUs, smartmeters).

- New analysis, algorithms and methodologies should be developed and adopted, i.e., several new opportunies for research, business and training.

- Inter-area research with great potential of collaboration

References

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• Y. Chakhchoukh, V. Vittal and G. Heydt, “PMU based State Estimation byIntegrating Correlation”, IEEE Transactions on Power Systems, 29(2): 617-626,March 2014.

• Y. Chakhchoukh and H. Ishii, “Coordinated cyber-attacks on the measurementfunction in hybrid state estimation,” IEEE Transactions on Power Systems, 30(5):2487-2497, Sept. 2015.

• Y. Chakhchoukh and H. Ishii, “Enhancing robustness to cyber-attacks in powersystems through multiple least trimmed squares state estimations,” IEEETransactions on Power Systems, to appear 2016.

• Y. Chakhchoukh, V. Vittal, G. Heydt and H. Ishii, “LTS-based Robust Hybrid SEIntegrating Correlation”, IEEE Transactions on Power Systems, under review.

• Y. Chakhchoukh, S. Liu, M. Sugiyama and H. Ishii, “Statistical Outlier Detection forDiagnosis of Cyber Attacks in Power State Estimation”, IEEE Power & Energy SocietyGeneral Meeting, 2016.

References

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• Y. Liu, M. K. Reiter, and P. Ning, “False data injection attacks against stateestimation in electric power grids,” in Proc. 16th ACM Conf. on Computer andCommunications Security, 2009.

• V. Murugessen, Y. Chakhchoukh, V. Vittal G.T. Heydt, N. Logic, and S. Sturgill,“PMU Data Buffering For Power System State Estimators”, IEEE Power andEnergy Technology Systems Journal, to appear 2015.

• Q. Zhang, V. Vittal, G. Heydt, Y. Chakhchoukh, N. Logic and S. Sturgill, “The TimeSkew Problem in PMU measurements”, IEEE Power & Energy Society GeneralMeeting, San Diego, USA, July 2012.

• Q. Zhang, Y. Chakhchoukh, V. Vittal, G.T. Heydt, N. Logic, and S. Sturgill, “Impactof PMU measurement buffer length on state estimation and its optimization,”IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1657–1665, May 2013.

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Thank you.