Bayesian Estimation of Graph Signals

Kroizer, Ariel; Routtenberg, Tirza; Eldar, Yonina C.

doi:10.1109/tsp.2022.3159393

Cited by 16 publications

(4 citation statements)

References 61 publications

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“…In order to study graph signals and implement GSP tools, signals measured over the nodes of a network can be modeled as the outputs of graph filters [6], [21]- [24]. Similar to lineartime invariant (LTI) filters, graph filters can be classified as low-pass, band-pass, or high-pass, according to their frequency responses computed through the Graph Fourier Transform (GFT) [1].…”

Section: B Related Workmentioning

confidence: 99%

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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: B Related Workmentioning

confidence: 99%

“…In order to analyze data measured over graphs, we consider a graph signal model as an output of a graph filter h(L), as defined in (6), where the input graph signal is white Gaussian noise. This model is widely-used for different smooth graph filters, h(L), to represent various practical signals in networks (see, e.g.…”

Section: Measurement Modelmentioning

confidence: 99%

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

Dabush

Kroizer

Routtenberg

2023

Sensors

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“…GSP is a new and emerging field that extends concepts and techniques from traditional digital signal processing (DSP) to data on graphs. GSP theory includes methods such as the Graph Fourier Transform (GFT), graph filters [44]- [46], and sampling and recovery of graph signals [47]- [49]. By leveraging the graphical properties of the states in PSSE, the works in [39], [40], [50], [51] present GSPbased detectors that are able to detect existing unobservable FDI attacks.…”

Section: Introductionmentioning

confidence: 99%

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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“…Therefore, the main objective of this study is to solve these limitations and to try to achieve high accuracy in reconstructing noisy or missing real data: a Bayesian estimator of signal on graph has been derived [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

Pavement Distress Estimation via Signal on Graph Processing

Bruno

Colonnese

Scarano

et al. 2022

Sensors

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A comprehensive representation of the road pavement state of health is of great interest. In recent years, automated data collection and processing technology has been used for pavement inspection. In this paper, a new signal on graph (SoG) model of road pavement distresses is presented with the aim of improving automatic pavement distress detection systems. A novel nonlinear Bayesian estimator in recovering distress metrics is also derived. The performance of the methodology was evaluated on a large dataset of pavement distress values collected in field tests conducted in Kazakhstan. The application of the proposed methodology is effective in recovering acquisition errors, improving road failure detection. Moreover, the output of the Bayesian estimator can be used to identify sections where the measurement acquired by the 3D laser technology is unreliable. Therefore, the presented model could be used to schedule road section maintenance in a better way.

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Bayesian Estimation of Graph Signals

Cited by 16 publications

References 61 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

Pavement Distress Estimation via Signal on Graph Processing

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