A Fault Location Method Based on Polynomial Chaos Expansion for Non-uniform Power Transmission Lines With Uncertainty Parameters

This article introduces a novel fault location method utilizing polynomial-chaos expansion (PCE) designed specifically for non-uniform transmission lines affected by uncertain parameters. It considers the uncertain parameters arising from height and ground conductivity in transmission lines, examining their impact on conventional fault location methods, such as natural frequency and full-transient analysis approach. These uncertainties lead to considerable location errors, particularly magnified with increasing fault distances. To address this issue, we propose a fault location approach based on PCE and correlation estimation. Simulations cover fault distances ranging from tens to hundreds of kilometers, considering variations in non-uniform line section-lengths, and examining scenarios with single and multiple conductors. Results demonstrate that the proposed method exhibits robustness across different degrees of uncertainty parameters in non-uniform settings, reducing the relative location error to below 1%. In terms of computational efficiency, the PCE method can accelerate calculations by up to 12 times compared to the Monte Carlo method. Furthermore, the PCE method has been validated using fault transient data from an actual 220 kV power line achieving a location error of 2.41%, which demonstrates its practical applicability in real-world power grid scenarios.

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Publication Details

Journal
IEEE Transactions on Electromagnetic Compatibility
Published
2024-08-26
DOI
https://doi.org/10.1109/temc.2024.3423006
Citations
5
Primary Topic
Power Systems Fault Detection
Type
article
Field-Weighted Citation Impact
1.64

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article

A Fault Location Method Based on Polynomial Chaos Expansion for Non-uniform Power Transmission Lines With Uncertainty Parameters

Shaoyin He, Andréa Cozza, Yu Song, Yan‐Zhao Xie et al.
5 citations
IEEE Transactions on Electromagnetic Compatibility
Power Systems Fault Detection
1.64
article

A Fault Location Method Based on Polynomial Chaos Expansion for Non-uniform Power Transmission Lines With Uncertainty Parameters

Shaoyin He, Andréa Cozza, Yu Song, Yan‐Zhao Xie, Zhaoyang Wang
article en
5 citations

Abstract

This article introduces a novel fault location method utilizing polynomial-chaos expansion (PCE) designed specifically for non-uniform transmission lines affected by uncertain parameters. It considers the uncertain parameters arising from height and ground conductivity in transmission lines, examining their impact on conventional fault location methods, such as natural frequency and full-transient analysis approach. These uncertainties lead to considerable location errors, particularly magnified with increasing fault distances. To address this issue, we propose a fault location approach based on PCE and correlation estimation. Simulations cover fault distances ranging from tens to hundreds of kilometers, considering variations in non-uniform line section-lengths, and examining scenarios with single and multiple conductors. Results demonstrate that the proposed method exhibits robustness across different degrees of uncertainty parameters in non-uniform settings, reducing the relative location error to below 1%. In terms of computational efficiency, the PCE method can accelerate calculations by up to 12 times compared to the Monte Carlo method. Furthermore, the PCE method has been validated using fault transient data from an actual 220 kV power line achieving a location error of 2.41%, which demonstrates its practical applicability in real-world power grid scenarios.

IEEE Transactions on Electromagnetic CompatibilityVol. 66(5)
Centre National de la Recherche Scientifique (FR), Laboratoire de Génie Électrique et Électronique de Paris (FR), Sorbonne Université (FR), CentraleSupélec (FR), State Key Laboratory of Electrical Insulation and Power Equipment, Imperial College London (GB), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 16%
Power Systems Fault Detection
1.64
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