LAUSR

With the development of wind energy, the condition monitoring (CM) methods of wind turbines (WTs) based on supervisory control and data acquisition (SCADA) data have attracted much attention to detect potential faults. With the impact of complicated internal and external factors, the operation conditions of WTs are time-varying. Thus, it is necessary to adaptively update CM models in long-term operation. An adaptive WT CM method based on a multivariate state estimation technique (MSET) and continual learning (CL) is proposed, which is concise and suitable for practical application. MSET is used to build the nonparametric and high-accuracy normal behavior model. In the proposed CL strategy, new normal data will be temporarily stored in the data buffer to realize the adaptive update of the MSET model. Also, rules for missing and abnormal data are designed to stabilize update frequency and improve fault detection ability, respectively. The proposed method is validated using a real-world SCADA dataset with gearbox faults. The results show that the proposed method has higher estimation accuracy and lower false alarm rate (FAR) than other methods, and the proposed CL strategy has popularization potential. Related hyperparameters are discussed, and when using less training data, the proposed method still has a better performance.