Main content area

An anomaly identification model for wind turbine state parameters

Zhang, Yiyi, Zheng, Hanbo, Liu, Jiefeng, Zhao, Junhui, Sun, Peng
Journal of cleaner production 2018 v.195 pp. 1214-1227
algorithms, energy, models, prediction, wind turbines
Identifying the anomalies of wind turbine (WT) and maintaining in time will improve the reliability of wind turbine and the efficiency of energy use, however it is difficult toidentify the wind turbine's abnormal operation by the traditional threshold settings because the anomalies can be induced by multiple factors. Therefore, this paper presents an anomaly identification model for wind turbine state parameters, and the model can identify abnormal state which the fluctuation range of the condition parametersis within the SCADA alarm threshold. The main work is as follows: 1) in order to increase the accuracy of the prediction model, a novel BPNN model integrated genetic algorithm (GA) was employed to optimize the training method (called GABP method), data samples, and input parameter selection, respectively; 2) on this basis, the distribution characteristics of state parameter prediction errors were depicted by a T-location scale (TLS) distribution with the shift factor and elastic coefficient; 3)error abnormal index (EAI) is defined to quantify the abnormal level of the prediction error, which is used as an indicator of the wind turbine anomaly. The proposed method has been applied on areal 1.5 MW wind turbine, and the analysis shows that the proposed method is effective in wind turbine anomaly identification.