An anomaly identification model for wind turbine state parameters

作者：Zhang, Yiyi; Zheng, Hanbo^*; Liu, Jiefeng^*; Zhao, Junhui; Sun, Peng

来源：Journal of Cleaner Production, 2018, 195: 1214-1227.

DOI：10.1016/j.jclepro.2018.05.126

• Wind turbine
• Anomaly identification
• State parameters
• Back propagation neural networks (BPNN)
• T-location scale distribution
• Error abnormal index

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 (Us) 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.

• 出版日期2018-9-10
• 单位广西大学