{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:23:10Z","timestamp":1775067790671,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Grid Shanxi Electric Power Company through \u201cResearch on fault type identification and localization technology of distribution network based on multi-dimensional features\u201d","award":["5205M0230008"],"award-info":[{"award-number":["5205M0230008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>When single-phase ground faults occur in distribution systems, the fault characteristics of zero-sequence current signals are not prominent. They are quickly submerged in noise, leading to difficulties in fault section location. This paper proposes a method for fault section location in distribution networks based on improved empirical wavelet transform (IEWT) and GINs to address this issue. Firstly, based on kurtosis, EWT is optimized using the N-point search method to decompose the zero-sequence current signal into modal components. Noise is filtered out through weighted permutation entropy (WPE), and signal reconstruction is performed to obtain the denoised zero-sequence current signal. Subsequently, GINs are employed for graph classification tasks. According to the topology of the distribution network, the corresponding graph is constructed as the input to the GIN. The denoised zero-sequence current signal is the node input for the GIN. The GIN autonomously explores the features of each graph structure to achieve fault section location. The experimental results demonstrate that this method has strong noise resistance, with a fault section location accuracy of up to 99.95%, effectively completing fault section location in distribution networks.<\/jats:p>","DOI":"10.3390\/info15100650","type":"journal-article","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T11:19:18Z","timestamp":1729163958000},"page":"650","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Method for Single-Phase Ground Fault Section Location in Distribution Networks Based on Improved Empirical Wavelet Transform and Graph Isomorphic Networks"],"prefix":"10.3390","volume":"15","author":[{"given":"Chen","family":"Wang","sequence":"first","affiliation":[{"name":"State Grid Yuncheng Power Supply Company, Yuncheng 044400, China"}]},{"given":"Lijun","family":"Feng","sequence":"additional","affiliation":[{"name":"State Grid Yuncheng Power Supply Company, Yuncheng 044400, China"}]},{"given":"Sizu","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China"}]},{"given":"Guohui","family":"Ren","sequence":"additional","affiliation":[{"name":"State Grid Yuncheng Power Supply Company, Yuncheng 044400, China"}]},{"given":"Wenyao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"54035","DOI":"10.1109\/ACCESS.2020.2980573","article-title":"Two-terminal Fault Location Method of Distribution Network Based on Adaptive Convolution Neural Network","volume":"8","author":"Liang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cheng, H.-J., Meng, H., and Jiang, P.-J. 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