Estimation of the Admittance Matrix in Power Systems Under Laplacian and Physical Constraints

Halihal, Morad; Routtenberg, Tirza

doi:10.1109/icassp43922.2022.9747489

Cited by 7 publications

(6 citation statements)

References 58 publications

(102 reference statements)

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“…Second, the topology of the network is assumed to be known, which may not always be the case, particularly for distribution systems. In such cases, methods for estimating the topology [ 29 , 70 ] could be used. Third, the Gauss–Newton algorithm used in this study may be computationally expensive for large networks.…”

Section: Discussionmentioning

confidence: 99%

State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools

Dabush

Kroizer

Routtenberg

2023

Sensors

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This paper considers the problem of estimating the states in an unobservable power system, where the number of measurements is not sufficiently large for conventional state estimation. Existing methods are either based on pseudo-data that is inaccurate or depends on a large amount of data that is unavailable in current systems. This study proposes novel graph signal processing (GSP) methods to overcome the lack of information. To this end, first, the graph smoothness property of the states (i.e., voltages) is validated through empirical and theoretical analysis. Then, the regularized GSP weighted least squares (GSP-WLS) state estimator is developed by utilizing the state smoothness. In addition, a sensor placement strategy that aims to optimize the estimation performance of the GSP-WLS estimator is proposed. Simulation results on the IEEE 118-bus system show that the GSP methods reduce the estimation error magnitude by up to two orders of magnitude compared to existing methods, using only 70 sampled buses, and increase of up to 30% in the probability of bad data detection for the same probability of false alarms in unobservable systems The results conclude that the proposed methods enable an accurate state estimation, even when the system is unobservable, and significantly reduce the required measurement sensors.

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Section: Discussionmentioning

confidence: 99%

State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools

Dabush

Kroizer

Routtenberg

2023

Sensors

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“…the graph. The GSP-based detector in ( 16) was implemented in [39] with the ideal GHPF defined in (11), and in [50] with the graph TV filter from (10), both with L = B.…”

Section: Gsp-based Fdi Attack Detectionmentioning

confidence: 99%

“…The same assumption is required for generating the unobservable FDI attack in (13) [2], [63]. The assumption that H (and consequently its submatrix B) is known or can be estimated from historical data [7]- [10] gives the adversary more power than usually is possible in reality; We note that this is a well-adopted practice in the cybersecurity community, which increases the system's resilience.…”

Section: Remarks 1) Known Topologymentioning

confidence: 99%

“…However, BDD methods cannot detect well-designed attacks, named unobservable false data injection (FDI) attacks [3], [4]. Unobservable FDI attacks are achieved by manipulating measurements based on the power network topology [3], where the topology matrix is either known or can be estimated from historical data [5]- [8]. Unobservable FDI attacks may inflict severe damage by influencing the PSSE [4], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…However, BDD methods are not able to detect well-designed attacks, known as unobservable false data injection (FDI) attacks [2], [3], which can cause significant damage by misleading the PSSE system [4]- [6]. These attacks are achieved by manipulating measurements based on the power network topology [2], where the topology matrix is either known or can be estimated from historical data [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

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Structural-Constrained Methods for the Identification of False Data Injection Attacks in Power Systems

Morgenstern

Routtenberg

2022

IEEE Access

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Power system functionality is determined on the basis of power system state estimation (PSSE). Thus, corruption of the PSSE may lead to severe consequences, such as disruptions in electricity distribution, maintenance damage, and financial losses. Classical bad data detection (BDD) methods, developed to ensure PSSE reliability, are unable to detect well-designed attacks, named unobservable false data injection (FDI) attacks. In this paper, we develop novel structural-constrained methods for the detection of unobservable FDI attacks and the identification of the attacked buses. The proposed methods are based on formulating structural, sparse constraints on both the attack and the system loads. First, we exploit these constraints in order to compose an appropriate model selection problem. Then, we develop the associated generalized information criterion (GIC) for this problem. However, the GIC method's computational complexity grows exponentially with the network size, which may be prohibitive for large networks. Therefore, based on the proposed structural and sparse constraints, we develop two novel lowcomplexity methods for the practical identification of unobservable FDI attacks: 1) a modification of the state-of-the-art orthogonal matching pursuit (OMP) method; and 2) a method that utilizes the graph Markovian property in power systems, i.e. the second-neighbor relationship between the power data at the system's buses. In order to analyze the performance of the proposed methods, the appropriate oracle and clairvoyant detectors are also derived. The performance of the proposed methods is evaluated on the IEEE-30 bus test case. INDEX TERMSAttack detection and identification, false data injection (FDI) attacks, graph Markovian property, model selection, structural constraints,

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Backward–forward sweep inverse power flow for distribution line parameters estimation with metering data

Leal,

Carvalho

2024

IET Smart Grid

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The transition towards the smart grid requires better information on the electrical networks at the distribution system level. Information about electrical line parameters is a critical part of such information. An algorithm for estimating electric line parameters of medium‐voltage distribution networks is proposed. The algorithm relies upon active/reactive power and nodal voltage magnitude data from advanced metering infrastructure and combines an Ordinary Least Squares estimator with the classical Backward–Forward Sweep load‐flow method to iteratively estimate the admittance parameters of the electrical network components. The algorithm is proposed to be applied to balanced loading situations of networks where zero‐injection non‐monitored nodes may exist. Simulations using the IEEE 33‐bus system are used to demonstrate the effectiveness of the method under different loading situations and measurement noise levels.

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Estimation of the Admittance Matrix in Power Systems Under Laplacian and Physical Constraints

Cited by 7 publications

References 58 publications

State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools

State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools

Structural-Constrained Methods for the Identification of False Data Injection Attacks in Power Systems

Backward–forward sweep inverse power flow for distribution line parameters estimation with metering data

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