{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T09:18:05Z","timestamp":1773825485455,"version":"3.50.1"},"reference-count":12,"publisher":"World Scientific Pub Co Pte Ltd","issue":"05","funder":[{"DOI":"10.13039\/501100013096","name":"Science and Technology Project of State Grid","doi-asserted-by":"publisher","award":["2020YF-69"],"award-info":[{"award-number":["2020YF-69"]}],"id":[{"id":"10.13039\/501100013096","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:p> It is important to locate the fault distance and identify the fault types quickly, take effective measures to maintain line stability, and minimize the losses timely when there are short-circuit faults in transmission lines. For this purpose, a method based on deep learning is proposed for short-circuit faults identification in the transmission line. According to the similarity of samples in the reconstruction phase, a minimum neighborhood sample set is selected from the massive samples firstly, and then, the samples are trained using the back propagation algorithm along time in a recurrent neural network (RNN) with long-short term memory (LSTM) units. Compared with existing algorithms, the experimental results show that this algorithm meets the requirements of rapid fault diagnosis in the case of variable parameters, and higher fault type recognition accuracy and lower fault distance error can be obtained. <\/jats:p>","DOI":"10.1142\/s0218001422520097","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T09:24:10Z","timestamp":1648545850000},"source":"Crossref","is-referenced-by-count":12,"title":["Method of Short-Circuit Fault Diagnosis in Transmission Line Based on Deep Learning"],"prefix":"10.1142","volume":"36","author":[{"given":"Tong","family":"Li","sequence":"first","affiliation":[{"name":"Northeastern University, Shenyang 110819, Liaoning, P. R. China"},{"name":"Electric Power Research Institute of State Grid Liaoning, Electric Power Supply Co.,Ltd, Shenyang 110004, Liaoning P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hai","family":"Zhao","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang 110819, Liaoning, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Grid Liaoning Electric Power Supply Co., Ltd, Shenyang 110004, Liaoning, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5965-2623","authenticated-orcid":false,"given":"Shidong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Engineering, Shenyang 110136, Liaoning, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Engineering, Shenyang 110136, Liaoning, P. R. 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Power Syst."}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001422520097","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T09:30:33Z","timestamp":1652779833000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218001422520097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,28]]},"references-count":12,"journal-issue":{"issue":"05","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["10.1142\/S0218001422520097"],"URL":"https:\/\/doi.org\/10.1142\/s0218001422520097","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,28]]},"article-number":"2252009"}}