LAUSR

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.