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In this paper, by extracting the actual operation data of the wind turbine in Supervisory Control and Data Acquisition (SCADA) system, the Bidirectional Recurrent Neural Networks (BRNN) is used to establish the wind turbine operation prediction model. By eliminating abnormal data points caused by accidental factors through box diagram, the fault risk threshold of wind turbine components is optimized. Then, based on the residual between the actual value and the measured value of the large sliding window, the early fault warning is realized according to Wright criterion. Finally, the model proposed in this paper is applied to the actual wind turbine, which proves the reliability and accuracy of the method.<\/jats:p>","DOI":"10.3233\/jifs-190642","type":"journal-article","created":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T11:00:10Z","timestamp":1571396410000},"page":"3389-3401","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["Early fault warning of wind turbine based on BRNN and large sliding window"],"prefix":"10.1177","volume":"38","author":[{"given":"Liang","family":"Tao","sequence":"first","affiliation":[{"name":"Hebei University of Technology, College of Artificial Intelligence, Tianjin, China"}]},{"given":"Qian","family":"Siqi","sequence":"additional","affiliation":[{"name":"Hebei University of Technology, College of Artificial Intelligence, Tianjin, China"}]},{"given":"Meng","family":"Zhaochao","sequence":"additional","affiliation":[{"name":"Hebei University of Technology, College of Artificial Intelligence, Tianjin, China"}]},{"given":"Xie","family":"Gao Feng","sequence":"additional","affiliation":[{"name":"Hebei University of Technology, College of Artificial Intelligence, Tianjin, China"}]}],"member":"179","published-online":{"date-parts":[[2019,10,16]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2012.03.003"},{"issue":"3","key":"e_1_3_2_3_2","first-page":"3","article-title":"Operation and maintenance costs compared and revealed","volume":"19","author":"Milborrow D.","year":"2006","unstructured":"MilborrowD., Operation and maintenance costs compared and revealed, Wind Stats 19(3) (2006), 3.","journal-title":"Wind Stats"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.phpro.2012.02.005"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2015.2512843"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/en81012100"},{"issue":"11","key":"e_1_3_2_7_2","first-page":"88","article-title":"He Chengbing and Gu Yuhuang, Real-time health assessment method for wind turbine based on operation condition identification[J]","volume":"33","author":"Yuliang Dong","year":"2013","unstructured":"YuliangDong, YaqiongLi and HaibinCao, He Chengbing and Gu Yuhuang, Real-time health assessment method for wind turbine based on operation condition identification[J], Journal of Electrical Engineering of China 33(11) (2013), 88\u201395.","journal-title":"Journal of Electrical Engineering of China"},{"issue":"09","key":"e_1_3_2_8_2","first-page":"2389","article-title":"[J]. 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