Wind Turbine Condition Monitoring Based on Bagging Ensemble Strategy and KNN Algorithm

Abstract

The gearbox is an important component of a wind turbine (WT). Once the gearbox is damaged, problems such as long-term maintenance and high maintenance costs will occur. Therefore, it is necessary to carry out on-line condition monitoring (CM) of WTs. Because a large amount of data is accumulated by the supervisory control and data acquisition (SCADA) system, CMs based on data-driven methods have been widely investigated. In this paper, a CM method that is based on the KNN regression method and bagging ensemble strategy is proposed. The proposed method is validated by SCADA data collected from a field WT. The results show that the ensemble model can achieve the desired estimation accuracy and improve the operation efficiency by approximately 30%.