Fault Location Using Wide-Area Measurements and Sparse Estimation

Feng, Guangyu; Abur, Ali

doi:10.1109/tpwrs.2015.2469606

Cited by 67 publications

(26 citation statements)

References 41 publications

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“…Using the voltage/current phasors at the line terminals is the most straightforward approach to estimate the fault location (e.g., [3], [4], [5], [6]). However, despite the straightforward solutions provided by the phasor-based fault location methods, their accuracy might be affected by the fault resistance, configuration of the line, load unbalance, and the presence of distributed generation [2].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Influence of Losses on the Performance of the Electromagnetic Time Reversal Fault Location Method

Razzaghi

Lugrin

Rachidi

et al. 2017

IEEE Trans. Power Delivery

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Abstract-Electromagnetic time reversal (EMTR) has beenshown to be an efficient method for locating faults in AC and DC power grids. In the available literature, the back-propagation medium has been considered to have identical losses as the directtime medium. However, the telegrapher's equations describing the travelling wave propagation are time-reversal invariant if and only if inverted losses are considered in the back propagation phase. This paper presents an analysis of the impact of losses on the performance of the EMTR-based fault location method for power networks. In this respect, three back-propagation models are proposed, analyzed and compared. It is shown that a lossy back-propagation model, for which the wave equations are not rigorously time-reversal invariant, results in accurate fault locations. Finally, an EMTR fault location system based on the lossy back-propagation model and a fast electromagnetic transient simulation platform is developed and its performances validated.

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Section: Introductionmentioning

confidence: 99%

Assessment of the Influence of Losses on the Performance of the Electromagnetic Time Reversal Fault Location Method

Razzaghi

Lugrin

Rachidi

et al. 2017

IEEE Trans. Power Delivery

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“…We present a distributed DSSE algorithm to identify the faulted subgraph efficiently, and this method significantly reduces the searching scale and speeds up the subsequent fault location procedure. Further, we conveniently determine a faulted line by applying a 5-13 between μPMUs 2 and 3 3 13-21 between μPMUs 3 and 4 4 13-44 between μPMUs 3 and 5 5 22-29 between μPMUs 6 and 7 6 22-39 between μPMUs 6 and 8 hierarchical graph-subgraph-path structure to the DSSE method.…”

Section: Discussionmentioning

confidence: 99%

“…The core of the proposed method is to determine the faulted line by comparing the weighted measurement residuals (WMRs) of DSSE in different topologies/graphs. This idea, as a typical application of state estimation, is proposed in [2], [3], and [19], where the power systems are observable by an adequate number of PMUs. In comparison, the proposed method only requires a limited number of μPMUs in distribution networks for such an application.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Faulted Line Identification Using Micro-PMU Data in Distribution Systems

Zhang

Wang

Khodayar

2020

IEEE Trans. Smart Grid

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Motivated by increasing penetration of distributed generators (DGs) and fast development of micro-phasor measurement units (μPMUs), this paper proposes a novel graphbased faulted line identification algorithm using a limited number of μPMUs in distribution networks. The core of the proposed method is to apply advanced distribution system state estimation (DSSE) techniques integrating μPMU data to the fault location. We propose a distributed DSSE algorithm to efficiently restrict the searching region for the fault source in the feeder between two adjacent μPMUs. Based on the graph model of the feeder in the reduced searching region, we further perform the DSSE in a hierarchical structure and identify the location of the fault source. Also, the proposed approach captures the impact of DGs on distribution system operation and remains robust against highlevel noises in measurements. Numerical simulations verify the accuracy and efficiency of the proposed method under various fault scenarios covering multiple fault types and fault impedances.

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“…where S is the set of measured buses,ψ j is defined in (11) with s ∈ S, y j i ∈ R m is unity if the label of the j-th dataset is i, and it is zero otherwise, andȳ j i = f Θ,S (ψ j ) is the output probability of CNN for the fault location of the j-th dataset to be at line i. f Θ,S (·) denotes functions of (13) ∼ (16) parameterized by Θ given the set S to estimate the probability. λ is the regularization coefficient.…”

Section: B Training Processmentioning

confidence: 99%

“…The authors in [11], [12] largely reduce the computational complexity by formulating the search process into a sparse optimization problem, and the values larger than a pre-defined…”

mentioning

confidence: 99%

Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks

Deka

Chertkov

et al. 2019

IEEE Trans. Power Syst.

133

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Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging. Existing localization techniques in this area rely on simplistic assumptions, such as static loads, or require much higher sampling rates or total measurement availability. This paper proposes a faulted line localization method based on a Convolutional Neural Network (CNN) classifier using bus voltages. Unlike prior data-driven methods, the proposed classifier is based on features with physical interpretations that improve the robustness of the location performance. The accuracy of our CNN based localization tool is demonstrably superior to other machine learning classifiers in the literature. To further improve the location performance, a joint phasor measurement units (PMU) placement strategy is proposed and validated against other methods. A significant aspect of our methodology is that under very low observability (7% of buses), the algorithm is still able to localize the faulted line to a small neighborhood with high probability. The performance of our scheme is validated through simulations of faults of various types in the IEEE 39-bus and 68bus power systems under varying uncertain conditions, system observability, and measurement quality.

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Fault Location Using Wide-Area Measurements and Sparse Estimation

Cited by 67 publications

References 41 publications

Assessment of the Influence of Losses on the Performance of the Electromagnetic Time Reversal Fault Location Method

Assessment of the Influence of Losses on the Performance of the Electromagnetic Time Reversal Fault Location Method

Graph-Based Faulted Line Identification Using Micro-PMU Data in Distribution Systems

Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks

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