A two steps procedure in state estimation gross error detection, identification, and correction

Bretas, N.G.; Bretãs, Arturo S.

doi:10.1016/j.ijepes.2015.05.044

Cited by 51 publications

(17 citation statements)

References 18 publications

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“…In the classical WLS approach, the best estimate of the state vector in (1) is found by minimising the cost function

J false(x false)

J false(x false) = {(∥∥, z - h false(x false))}_{{boldΣ}_{SE}^{- 1}}^{2} = false[z - h false(x false)]^{normal′} {boldΣ}_{SE}^{- 1} false[z - h false(x false) false]

where

Σ

is the covariance matrix of the measurements. In this paper, we consider the standard deviation of each measurement to be 1% of the measurement magnitude, which has been shown to improve the detection of bad data [31]. The covariance submatrix for zero injection measurements is calculated, as shown in (4) and (5).…”

Section: Hybrid Physics Model‐based Data‐driven Frameworkmentioning

confidence: 99%

Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid security

et al. 2020

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This paper presents a hybrid data-driven physics model-based framework for real time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, it's real time monitoring becomes more vulnerable to cyber attacks like false data injections (FDI). Although smart grids cyber-physical security has an extensive scope, this paper focuses on FDI attacks, which are modeled as bad data. State of the art strategies for FDI detection in real time monitoring rely on physics model-based weighted least squares state estimation solution and statistical tests. This strategy is inherently vulnerable by the linear approximation and the companion statistical modeling error, which means it can be exploited by a coordinated FDI attack. In order to enhance the robustness of FDI detection, this paper presents a framework which explores the use of data-driven anomaly detection methods in conjunction with physics model-based bad data detection via data fusion. Multiple anomaly detection methods working at both the system level and distributed local detection level are fused. The fusion takes into consideration the confidence of the various anomaly detection methods to provide the best overall detection results. Validation considers tests on the IEEE 118 bus system.

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“…In the classical WLS approach, the best estimate of the state vector in (1) is found by minimising the cost function

J false(x false)

J false(x false) = {(∥∥, z - h false(x false))}_{{boldΣ}_{SE}^{- 1}}^{2} = false[z - h false(x false)]^{normal′} {boldΣ}_{SE}^{- 1} false[z - h false(x false) false]

where

Σ

Section: Hybrid Physics Model‐based Data‐driven Frameworkmentioning

confidence: 99%

Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid security

et al. 2020

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“…This is because the performance of the state estimator would be deteriorated, providing inaccurate information to the power system operators. In this sense, a scheduled routine can be performed [22] during the steady state operation of the system for avoiding the presence of PMU anomalies during the execution of the proposed ICI and restoration scheme.…”

Section: Discussionmentioning

confidence: 99%

A Real-Time Controlled Islanding and Restoration Scheme Based on Estimated States

Demetriou

Asprou

Kyriakides

2019

IEEE Trans. Power Syst.

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Power system operators are facing major challenges today to keep the system operating at the admissible limits. Recent blackouts demonstrated the need for a systematic study and design of a comprehensive system control strategy. Intentional Controlled Islanding (ICI) has been proposed as an effective corrective control action of final resort to save the system from a partial or a complete blackout. ICI limits the occurrence and consequences of blackouts by splitting the power system into a group of smaller, stable, and sustainable subsystems, also called islands. After a controlled system separation, power system operators should resynchronize and reconnect each island to restore the system. In this sense, real-time knowledge of the operating condition of the islands is required. In this paper, a realtime ICI and restoration scheme is proposed. The proposed scheme consists of an ICI algorithm that finds islanding solutions with minimal power-flow disruption while considering power system restoration constraints (e.g., blackstart availability, sufficient generation capacity and observability), a real-time state estimator that monitors the system before and after the islanding, and a restoration process. The proposed ICI and restoration scheme is tested using the dynamic IEEE 39-and 118-bus test systems.

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“…The power system with n buses and m measurements is modeled as a set of non-linear equations as follows [19]:…”

Section: State Estimation With the Innovation Conceptmentioning

confidence: 99%

Multi-Area State Estimation: A Distributed Quasi-Static Innovation-Based Model with an Alternative Direction Method of Multipliers

et al. 2021

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In the modern power system networks, grid observability has greatly increased due to the deployment of various metering technologies. Such technologies enhanced the real-time monitoring of the grid. The collection of observations are processed by the state estimator in which many applications have relied on. Traditionally, state estimation on power grids has been done considering a centralized architecture. With grid deregulation, and awareness of information privacy and security, much attention has been given to multi-area state estimation. Considering such, state-of-the-art solutions consider a weighted norm of residual measurement model, which might hinder masked gross errors contained in the null-space of the Jacobian matrix. Towards the solution of this, a distributed innovation-based model is presented. Measurement innovation is used towards error composition. The measurement error is an independent random variable, where the residual is not. Thus, the masked component is recovered through measurement innovation. Model solution is obtained through an Alternating Direction Method of Multipliers (ADMM), which requires minimal information communication. The presented framework is validated using the IEEE 14 and IEEE 118 bus systems. Easy-to-implement model, build-on the classical weighted norm of the residual solution, and without hard-to-design parameters highlight potential aspects towards real-life implementation.

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A two steps procedure in state estimation gross error detection, identification, and correction

Cited by 51 publications

References 18 publications

Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid security

Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid security

A Real-Time Controlled Islanding and Restoration Scheme Based on Estimated States

Multi-Area State Estimation: A Distributed Quasi-Static Innovation-Based Model with an Alternative Direction Method of Multipliers

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