{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:50:10Z","timestamp":1754157010096,"version":"3.41.2"},"reference-count":10,"publisher":"Emerald","issue":"8","license":[{"start":{"date-parts":[[2010,8,10]],"date-time":"2010-08-10T00:00:00Z","timestamp":1281398400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,8,10]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to define a new method (grey relational analysis (GRA)) for extracting pattern samples of dissolved gases in power transformer oil, then a hybrid algorithm of the back\u2010propagation (BP) network and fuzzy genetic algorithm\u2010artificial neural network (FGA\u2010ANN) is used to power transformer fault diagnosis based on extracted pattern samples.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>The existing manners (e.g. international electro technical commission triple\u2010ratio method), in practice, have certain faultiness due to the ambiguity of the inference and insufficient standard for judgment. So GRA method is chosen to solve a problem of optimal pattern samples data, then a hybrid algorithm of the BP network and FGA\u2010ANN is developed to optimize initial weights and to enable fast convergence of the BP network, and lastly, this algorithm is applied to the classification of dissolved gas analysis (DGA) data and power transformer fault diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>If possible, the results should be accompanied by significance. For comparative studies, the proposed scheme does not require the three ratio code and high diagnosis accuracy is obtained. In addition, useful information is provided for future fault trends and multiple faults analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title><jats:p>Accessibility and availability of data are the main limitations which model will be applied.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>This paper provides useful advice for power transformer fault diagnosis method based on DGA data.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The new method of optimal choice of options of pattern samples due to GRA. The paper is aimed at optimized samples data classified and abandons the traditional ratio method.<\/jats:p><\/jats:sec>","DOI":"10.1108\/03684921011063510","type":"journal-article","created":{"date-parts":[[2010,8,28]],"date-time":"2010-08-28T07:17:31Z","timestamp":1282979851000},"page":"1235-1244","source":"Crossref","is-referenced-by-count":3,"title":["Research on fault diagnosis method for transformer based on fuzzy genetic algorithm and artificial neural network"],"prefix":"10.1108","volume":"39","author":[{"given":"Zhenghong","family":"Peng","sequence":"first","affiliation":[]},{"given":"Bin","family":"Song","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"unstructured":"Chen, H. (2005), \u201cFault tolerant control for switched reluctance motor drive based on fuzzy logic\u201d, Advances in Systems Science and Applications, Vol. 5 No. 2, pp. 219\u201024.","key":"key2022021120184852500_b7"},{"unstructured":"Fan, J.S., Tao, Q. and Fang, T.J. (2000), \u201cGenetic algorithm optimize neural network based on structural risk minimization\u201d, Proceedings of the 3rd World Congress on Intelligent Control and Automation, Hefei, China, 28 June\u20102 July, pp. 948\u201052.","key":"key2022021120184852500_b5"},{"unstructured":"Gao, W.S., Yan, Z. and Tan, K.X. (2000), \u201cFault diagnosis of insulation in power transformer based on dissolved gas analysis method\u201d, Advanced Technology of Electrical Engineering and Energy, Vol. 19 No. 1, pp. 22\u20106.","key":"key2022021120184852500_b1"},{"unstructured":"Li, Q., Gao, C., Hu, Y. and Li, D. (2007), \u201cA data mending algorithm based on neural network optimized by genetic\u2010simulated annealing hybrid algorithm\u201d, Advances in Systems Science and Applications, Vol. 7 No. 1, pp. 132\u20106.","key":"key2022021120184852500_b6"},{"doi-asserted-by":"crossref","unstructured":"Lin, Y. and Liu, S.F. 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