{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:03:15Z","timestamp":1760058195503,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CHINA SOUTHERN POWER GRID","award":["1500002023030103SJ00102"],"award-info":[{"award-number":["1500002023030103SJ00102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Dry-type air-core reactors (DAR) are critical components in power systems but are prone to inter-turn short circuit faults which interrupt the symmetry of the winding structure. Inspired by the online detection of transformer winding deformation, the V-I method has been adapted to diagnose short circuit faults in reactors. However, the diagnostic criteria and thresholds of V-I method remain unclear. This paper presents a novel method for determining the threshold for detecting inter-turn short circuit faults in DAR, integrating V-I analysis with machine learning techniques. Specifically, Gradient Boosting Regression (GBR) is used to compute a standard diagnostic criterion value, and curve fitting is also used to define the threshold for identifying inter-turn short circuit faults. The experimental results demonstrate that this method effectively identifies fault conditions in DAR.<\/jats:p>","DOI":"10.3390\/sym17030459","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T07:48:37Z","timestamp":1742370517000},"page":"459","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Short Circuit Fault Detection in DAR Based on V-I Characteristic Graph and Machine Learning"],"prefix":"10.3390","volume":"17","author":[{"given":"Junlin","family":"Zhu","sequence":"first","affiliation":[{"name":"Electric Power Research Institute of China Southern Power Grid, Shenzhen 518118, China"}]},{"given":"Jiahui","family":"Yang","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute of China Southern Power Grid, Shenzhen 518118, China"}]},{"given":"Xiaojing","family":"Dang","sequence":"additional","affiliation":[{"name":"Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518001, China"}]},{"given":"Xiaqing","family":"Sun","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute of China Southern Power Grid, Shenzhen 518118, China"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute of China Southern Power Grid, Shenzhen 518118, China"}]},{"given":"Yuqian","family":"Song","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, Southwest University, Chongqing 400700, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4089-4470","authenticated-orcid":false,"given":"Zhongyong","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, Southwest University, Chongqing 400700, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Toader, D., Greconici, M., Vesa, D., Vintan, M., Solea, C., Maghet, A., and Tatai, I. 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