{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T17:16:09Z","timestamp":1773335769443,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Power Grid Co., Ltd.","award":["GDKJXM20240405"],"award-info":[{"award-number":["GDKJXM20240405"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Digital"],"abstract":"<jats:p>Dissolved gas analysis (DGA) provides valuable information for transformer condition monitoring, yet accurate multi-class fault identification remains challenging due to overlapping gas patterns and the sensitivity of classifier hyperparameters. This study proposes a hybrid optimization framework that combines Particle Swarm Optimization and Grey Wolf Optimization to tune the hyperparameters of a Support Vector Machine (SVM) for transformer fault diagnosis based on gas classification. The model is evaluated on a DGA dataset using a strict protocol that separates cross-validation\u2013based tuning from held-out test assessment. Experimental results show that the proposed hybrid PSO-GWO-SVM achieves superior diagnostic performance and more stable convergence compared with representative single-optimizer baselines, demonstrating its potential for practical transformer fault identification.<\/jats:p>","DOI":"10.3390\/digital6010024","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T10:53:31Z","timestamp":1773140011000},"page":"24","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Optimization Model for Transformer Fault Diagnosis Based on Gas Classification"],"prefix":"10.3390","volume":"6","author":[{"given":"Junju","family":"Lai","sequence":"first","affiliation":[{"name":"Qingyuan Power Supply Bureau, Guangdong Power Grid Corporation Limited, No. 38, Beijiang Road, Qingcheng District, Qingyuan 511500, China"}]},{"given":"Dongpeng","family":"Weng","sequence":"additional","affiliation":[{"name":"Qingyuan Power Supply Bureau, Guangdong Power Grid Corporation Limited, No. 38, Beijiang Road, Qingcheng District, Qingyuan 511500, China"}]},{"given":"Feng","family":"Xian","sequence":"additional","affiliation":[{"name":"Qingyuan Power Supply Bureau, Guangdong Power Grid Corporation Limited, No. 38, Beijiang Road, Qingcheng District, Qingyuan 511500, China"}]},{"given":"Yuandong","family":"Xie","sequence":"additional","affiliation":[{"name":"Qingyuan Power Supply Bureau, Guangdong Power Grid Corporation Limited, No. 38, Beijiang Road, Qingcheng District, Qingyuan 511500, China"}]},{"given":"Yujie","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 410082, China"}]},{"given":"Qian","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9346-7256","authenticated-orcid":false,"given":"Chao","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 410082, China"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1002\/tee.23197","article-title":"A Comparison between Artificial Intelligence Method and Standard Diagnosis Methods for Power Transformer Dissolved Gas Analysis Using Two Public Databases","volume":"15","author":"Zheng","year":"2020","journal-title":"IEEJ Trans. 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