文献类型：期刊文献

英文题名：Multiple Elastic Networks With Time Delays for Early Fault Detection and Prognostics

作者：Guo, Dongliang[1];Yang, Wen[2];Tao, Fengbo[1];Song, Bing[2];Liu, Hui[3];Sun, Lei[1];Wang, Jiale[2]

机构：[1]State Grid Jiangsu Elect Power Co Ltd, Res Inst, Nanjing 211103, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]Shanghai Power & Energy Storage Battery Syst Engn, Shanghai 200241, Peoples R China

年份：2020

卷号：8

起止页码：129387

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20203309041759);WOS:【SCI-EXPANDED(收录号:WOS:000551831400001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61703161, in part by the Fundamental Research Funds for the Central Universities under Grant 222201714031, and in part by the National Natural Science Foundation of Shanghai under Grant 19ZR1473200.

语种：英文

外文关键词：Energy storage; Power generation; Predictive models; Market research; Bayes methods; Object oriented modeling; Delay effects; Fault detection; data analysis; fault prognostics; fault diagnosis; process monitoring

摘要：Aiming at the problem of fault prognostics for the energy storage power station, this paper proposes a novel data-driven method named multiple elastic networks with time delays (MEN-TD). The proposed method can learn the status of the energy storage power station in advance and provide early detection of the fault. First, through the correlation analysis and the mechanism knowledge, the energy storage power station key parameter and corresponding key factors affecting the parameter are determined. Secondly, in order to predict the trend of the key parameter over a period of time and improve the prediction accuracy, the MEN-TD model is constructed. Then, based on the predicted values of the key parameter, compared with the control limit in the healthy status, the fault can be pre-warned in advance. Finally, through testing on the practical energy storage power station in Zhenjiang of China, the effectiveness and superiority of the proposed MEN-TD method are demonstrated.