{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:45:50Z","timestamp":1776811550736,"version":"3.51.2"},"reference-count":22,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,12,19]]},"abstract":"<jats:p>Although the global wind energy industry has made considerable progress in recent years, wind turbines suffer from frequent failures since the systems are complicated and the working conditions are far from being satisfactory. For the wind turbines to function well, it is imperative to study the overall status of the wind turbine unit, evaluate the performance of the wind farm, apply intelligent operation and maintenance technology, and improve operation and maintenance strategies on an on-going basis, all of which are based on the operation data of the unit. This paper focuses on the evaluation and extension of the lifetime of wind turbines. Based on relevant knowledge and theories from previous studies, an evaluation method based on big data was designed to do the evaluation, and the results of which were verified by real cases. With the duration of catastrophic failures taken into account, the proposed life prediction algorithm was proved to be effective. If the bearing runs for 34 days, the actual remaining life of wind turbines is 0.2 days. The number predicted for LRM is 0.8 days and that predicted for ILRM is 0.31 days. Compared with LRM, the prediction for ILRM is much more accurate.<\/jats:p>","DOI":"10.3233\/jcm226436","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T11:56:19Z","timestamp":1662465379000},"page":"1865-1873","source":"Crossref","is-referenced-by-count":1,"title":["Lifetime evaluation and extension of wind turbines based on big data"],"prefix":"10.66113","volume":"22","author":[{"given":"Jun","family":"Su","sequence":"first","affiliation":[{"name":"SPIC Jiangsu New Energy Co., Ltd., Yancheng, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Fang","sequence":"additional","affiliation":[{"name":"Shanghai Power Equipment Research Institute Co., Ltd., Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinglong","family":"Zhu","sequence":"additional","affiliation":[{"name":"SPIC Jiangsu New Energy Co., Ltd., Yancheng, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Power Equipment Research Institute Co., Ltd., Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Sun","sequence":"additional","affiliation":[{"name":"Shanghai Power Equipment Research Institute Co., Ltd., Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaying","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Power Equipment Research Institute Co., Ltd., Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"3","key":"10.3233\/JCM226436_ref1","first-page":"149","article-title":"Modelling and control of large wind turbine modellering OCH reglering AV stora vindkraftverk","volume":"3","author":"Zafar","year":"2017","journal-title":"Wind Energy."},{"issue":"4","key":"10.3233\/JCM226436_ref2","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1109\/TSTE.2014.2345059","article-title":"Wind turbine power curve modeling using advanced parametric and nonparametric methods","volume":"5","author":"Shokrzadeh","year":"2017","journal-title":"IEEE Trans Sustainable Energy."},{"issue":"2","key":"10.3233\/JCM226436_ref3","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s00202-015-0353-2","article-title":"Imperialist competitive algorithm for speed control of induction motor supplied by wind turbine","volume":"98","author":"Ali","year":"2016","journal-title":"Electr Eng."},{"issue":"1","key":"10.3233\/JCM226436_ref4","doi-asserted-by":"crossref","first-page":"45","DOI":"10.4028\/www.scientific.net\/AST.101.45","article-title":"Wind turbine fault detection through principal component analysis and statistical hypothesis testing","volume":"101","author":"Pozo","year":"2016","journal-title":"Adv Sci Technol."},{"issue":"1","key":"10.3233\/JCM226436_ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2514\/1.J054199","article-title":"Coriolis effect on dynamic stall in a vertical axis wind turbine","volume":"54","author":"Tsai","year":"2016","journal-title":"Aiaa J."},{"issue":"1","key":"10.3233\/JCM226436_ref6","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/JSYST.2014.2313810","article-title":"Coordinated control of wind turbine blade pitch angle and PHEVs using MPCs for load frequency control of microgrid","volume":"10","author":"Pahasa","year":"2016","journal-title":"IEEE Syst J."},{"issue":"3","key":"10.3233\/JCM226436_ref7","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/TSTE.2015.2418282","article-title":"Distributed model predictive control of a wind farm for optimal active power ControlPart I: Clustering-based wind turbine model linearization","volume":"6","author":"Zhao","year":"2015","journal-title":"IEEE Trans Sustainable Energy."},{"issue":"4","key":"10.3233\/JCM226436_ref8","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1049\/iet-rpg.2016.0248","article-title":"Using SCADA data for wind turbine condition monitoring \u2013 A review","volume":"11","author":"Tautz-Weinert","year":"2016","journal-title":"IET Renewable Power Gener."},{"issue":"11","key":"10.3233\/JCM226436_ref9","first-page":"1240","article-title":"Overview of big data technology research","volume":"47","author":"Zhang","year":"2014","journal-title":"Commun Technol."},{"issue":"17","key":"10.3233\/JCM226436_ref11","first-page":"2","article-title":"Analysis of big data technology on the increase of wind farm power generation","author":"Li","year":"2019","journal-title":"China Constr."},{"key":"10.3233\/JCM226436_ref12","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.compstruct.2015.08.137","article-title":"Damage and nonlinearities detection in wind turbine blades based on strain field pattern recognition. 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