Faulty feeder detection based on the composite factors in resonant grounding distribution system

Tang, Tao; Zhang, Xiangjun; Huang, Chun; Li, Zewen

doi:10.1016/j.epsr.2020.106578

Cited by 9 publications

(1 citation statement)

References 20 publications

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“…Among these methods, both data-driven principles and calculation are extremely complex, especially when their physical principles are not clear enough, which is detrimental to extending them to the identification of electrical topology. But references [93] presented an explicit physical meaning in the identification of the line-user relation of a low-voltage distribution network by way of the establishment of an optimization model based on Kirchhoff's current law and conversion of the model into a quadratic programming problem for solution.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Overview on topology identification technologies for a low-voltage distribution network

2024

Archives of Electrical Engineering

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The topology identification of low-voltage distribution networks is an important foundation for the intelligence of low-voltage distribution networks. Its accuracy fundamentally determines the effectiveness of functions such as power system state estimation, operational control, optimization planning, and intelligent electricity consumption. The low-voltage distribution network is composed of transformers, lines, and end users. The key task of topology identification is to distinguish the connection relationship between distribution transformers, low-voltage lines, and phase sequence with end users, which can be divided into transformer user relationship, line user relationship, and phase user relationship. At present, the main methods of low-voltage network topology identification can be divided into signal injection method and data analysis method. The signal injection method requires a large number of additional terminal devices and is difficult to promote. The data analysis method combines the characteristics of switch state, voltage, current, electrical energy, and other data to perform topology analysis. The commonly used methods include correlation analysis and feature learning. Finally, typical problems that urgently need to be solved in topology recognition and representation were proposed, providing a reference for the research and development of low-voltage distribution network topology automatic recognition technology.

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Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Overview on topology identification technologies for a low-voltage distribution network

2024

Archives of Electrical Engineering

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Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise

Gao,

Guo,

Lin

et al. 2024

Circuit Theory & Apps

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In addressing the quantization noise challenge in high impedance fault (HIF) localization within resonant distribution networks, we propose a cutting‐edge, explainable deep learning approach that significantly advances existing methods. This approach utilizes differential zero‐sequence voltage (DZSV) and zero‐sequence current (ZSC) and introduces a novel “Vague” classification to improve localization accuracy by effectively managing quantization noise‐distorted signals. This approach extends beyond the conventional binary classification of “Fault” and “Sound,” incorporating a multi‐scale feature attention (MFA) mechanism for enriched internal explainability and applying gradient‐weighted class activation mapping (Grad‐CAM) to visualize critical input areas precisely. Our model, validated in an industrial prototype, exhibits unparalleled adaptability across various environmental conditions, including environmental noise, variable sampling rates, and triggering deviations. Comparative analysis reveals that our approach outperforms existing methods in managing diverse fault scenarios.

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A faulty feeder selection method based on the slope of zero sequence current fitting curve for the non-effectively grounded system

Guo

Zhang

et al. 2023

Electric Power Systems Research

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Faulty feeder detection based on the composite factors in resonant grounding distribution system

Cited by 9 publications

References 20 publications

Overview on topology identification technologies for a low-voltage distribution network

Overview on topology identification technologies for a low-voltage distribution network

Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise

A faulty feeder selection method based on the slope of zero sequence current fitting curve for the non-effectively grounded system

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