Line Outage Localization Using Phasor Measurement Data in Transient State

García, María Teresa Iglesias; Catanach, Thomas A.; Wiel, Scott Vander; Bent, Russell; Lawrence, Earl

doi:10.1109/tpwrs.2015.2461461

Cited by 37 publications

(17 citation statements)

References 17 publications

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“…The problem studied in this paper is related to the problem of line failures detection using phase angle measurements [14,30,32,33,39]. Up to two line failures detection, under the DC power flow model, was studied in [32,33].…”

Section: Related Workmentioning

confidence: 99%

REACT to Cyber Attacks on Power Grids

Soltan

Yannakakis

Zussman

2019

IEEE Trans. Netw. Sci. Eng.

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Motivated by the recent cyber attack on the Ukrainian power grid, we study cyber attacks on power grids that affect both the physical infrastructure and the data at the control center. In particular, we assume that an adversary attacks an area by: (i) remotely disconnecting some lines within the attacked area, and (ii) modifying the information received from the attacked area to mask the line failures and hide the attacked area from the control center. For the latter, we consider two types of attacks: (i) data distortion: which distorts the data by adding powerful noise to the actual data, and (ii) data replay: which replays a locally consistent old data instead of the actual data. We use the DC power flow model and prove that the problem of finding the set of line failures given the phase angles of the nodes outside of the attacked area is strongly NP-hard, even when the attacked area is known. However, we introduce the polynomial time REcurrent Attack Containment and deTection (REACT) Algorithm to approximately detect the attacked area and line failures after a cyber attack. We numerically show that it performs very well in detecting the attacked area, and detecting single, double, and triple line failures in small and large attacked areas.

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Section: Related Workmentioning

confidence: 99%

REACT to Cyber Attacks on Power Grids

Soltan

Yannakakis

Zussman

2019

IEEE Trans. Netw. Sci. Eng.

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“…Other limitations in practice such as behaviors of human operators under time pressure, missing and contradicting information, and privacy concerns over data sharing can make this problem even harder. Assuming a small number of line failures, exhaustive search methods have been developed in [5], [6], [7] and [8] based on hypothesis testing, and in [9] based on logistic regression. To overcome the prohibitive computational complexity of exhaustive search methods, [10] has developed sparsity exploiting outage identification methods with overcomplete observations to identify sparse multi-line outages.…”

Section: Introductionmentioning

confidence: 99%

A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification

Zhao

Chen

Poor

2020

IEEE Trans. Smart Grid

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Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem. This is because the number of hypotheses grows exponentially with the network size. A new "Learning-to-Infer" method is developed for efficient inference of every line status in the network. Optimizing the line outage detector is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. A major advantage of the developed Learningto-Infer method is that the labeled data used for training can be generated in an arbitrarily large amount rapidly and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time multiline outage identification. The proposed methods are evaluated in the IEEE 30, 118 and 300 bus systems. Excellent performance in identifying multi-line outages in real time is achieved with a reasonably small amount of data.

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“…The problem studied in this paper is similar to the problem of line failures detection using phase angle measurements [5], [6], [7], [26]. Up to two line failures detection, under the DC power flow model, was studied in [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…In a recent work [26], a linear multinomial regression model was proposed as a classifier for a single line failure detection using transient voltage phase angles data. Due to the time complexity of the learning process for multiple line failures, this method is impractical for detecting higher order failures.…”

Section: Introductionmentioning

confidence: 99%

EXPOSE the Line Failures Following a Cyber-Physical Attack on the Power Grid

Soltan

Zussman

2019

IEEE Trans. Control Netw. Syst.

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Recent attacks on power grids demonstrated the vulnerability of the grids to cyber and physical attacks. To analyze this vulnerability, we study cyber-physical attacks that affect both the power grid physical infrastructure and its underlying Supervisory Control And Data Acquisition (SCADA) system. We assume that an adversary attacks an area by: (i) disconnecting some lines within that area, and (ii) obstructing the information (e.g., status of the lines and voltage measurements) from within the area to reach the control center. We leverage the algebraic properties of the AC power flows to introduce the efficient EXPOSE Algorithm for detecting line failures and recovering voltages inside that attacked area after such an attack. The EXPOSE Algorithm outperforms the state-of-the-art algorithm for detecting line failures using partial information under the AC power flow model in terms of scalability and accuracy. The main advantages of the EXPOSE Algorithm are that its running time is independent of the size of the grid and number of line failures, and that it provides accurate information recovery under some conditions on the attacked area. Moreover, it approximately recovers the information and provides the confidence of the solution when these conditions do not hold.

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Line Outage Localization Using Phasor Measurement Data in Transient State

Cited by 37 publications

References 17 publications

REACT to Cyber Attacks on Power Grids

REACT to Cyber Attacks on Power Grids

A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification

EXPOSE the Line Failures Following a Cyber-Physical Attack on the Power Grid

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