{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:29:42Z","timestamp":1777703382762,"version":"3.51.4"},"reference-count":25,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2015,7,27]],"date-time":"2015-07-27T00:00:00Z","timestamp":1437955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2015,7,29]]},"abstract":"<jats:p>This paper proposes a new method to estimate power loss characteristics of metal oxide\n          surge arresters. The method was used to compute surge arrester power loss curve based on\n          adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN).\n          Surge arrester operating history is an important factor that bears influence on its power\n          loss. Degradation was, in this paper, introduced as a new index to represent operating\n          history of metal oxide surge arrester. Therefore, applied voltage, temperature and\n          degradation factor were considered as inputs in ANFIS system and ANN models to obtain\n          accurate power loss which is a very important factor in surge arrester thermal stability.\n          Degrading effect was undertaken by measurement voltage and current in varistors degraded\n          by utilization in network. The results of the two artificial models were compared. Results\n          show that ANFIS was more accurate than ANN. This study shows that the power loss\n          characteristics of surge arrester are to a great extent accurately predictable using\n          proposed artificial model.<\/jats:p>","DOI":"10.3233\/ifs-151655","type":"journal-article","created":{"date-parts":[[2015,11,10]],"date-time":"2015-11-10T11:34:10Z","timestamp":1447155250000},"page":"1779-1786","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["ANFIS based model for power loss estimation of metal oxide surge          arrester"],"prefix":"10.1177","volume":"29","author":[{"given":"Seyyed","family":"Meysam Seyyedbarzegrar","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Bobol University of Technology, Babol, Iran"}]},{"given":"Mohammad","family":"Mirzaie","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Bobol University of Technology, Babol, 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