LAUSR

Wind turbines (WTs) are complex systems composed of multiple components. In order to assess the overall condition of a given WT, one may need to employ multiple models; such a solution, however, is deemed to be unwieldy and expensive to implement, especially for large-scale wind farms. This article proposes a novel heterogeneous stacked regressor (HET-SR) algorithm, which learns from optimal power curve data attained through a delicate signal processing scheme involving density-based spatial clustering of applications with noise (DBSCAN). The proposed method is thus a normal behavior model (NBM) capable of accurately assessing the health status of an entire WT through an overall health indicator such as the power curve. We compare this method with state-of-the-art methods and perform an extensive experimental work based on real operational data from the supervisory control and data acquisition (SCADA) system. The findings of this research work provide evidence on the effectiveness and superiority of the proposed method in terms of high accuracy in fault detection.