Wind turbine gearbox condition monitoring based on extreme gradient boosting

Abstract

Currently, supervisory control and data acquisition (SCADA) systems are deployed in most wind farms, with the low cost of data acquisition. However, SCADA data contains a lot of redundant and dirty information, which makes it difficult to observe the actual condition of Wind Turbine (WT) or WT's components directly. In this paper, a gearbox condition monitoring (CM) model of WT based on SCADA data is proposed. In the first stage, after data preprocessing, the prediction model of gearbox oil temperature is obtained based on healthy data with Extreme Gradient Boosting (XGBoost), and the absolute percentage error (APE) of oil temperature is the final observational variable. In the second stage, the Multivariate Quality Control Charts (MQCC) is used to generate the threshold to detect the fault symptoms, combined with the APE of healthy data. Afterwards, the CM framework is established, which is capable of identifying the abnomal state of gearboxes based on whether the APE of new data exceeds the threshold. Finally, the effectiveness of the gearbox CM model presented is demonstrated by examining 2 groups of WT from different wind farms in China.