Wind turbine condition monitoring based on a novel multivariate state estimation technique

Abstract

With the development of the wind energy industry, condition monitoring (CM) has become one of the important ways to improve the reliability of wind turbines (WTs). A data driven WTCM method based on a novel multivariate state estimation technique (MSET) is proposed, which can realize the fault early warning of WT components. In order to reduce the information redundancy of operational parameters, the maximum conditional mutual information (CMI) feature selection algorithm is applied to the training data of MSET. To improve the performance and flexibility of MSET, a dynamic memory matrix (MM) construction method based on k-nearest neighbor algorithm is proposed, which can provide a dynamic MM that varies in real-time with the current operational condition. A fast calculation method of the dynamic MM is also proposed, which can reduce the computation time by 15–26%. Two residual-based CM methods are proposed to realize fault early warning. The real-time method is based on the real-time residuals and if several consecutive residuals exceed the threshold, the fault alerts will be issued. The long-term method divides the historical residuals into day-level and analyzes them based on control charts. The proposed method is applied to two real cases of the gearbox and generator bearing overheating faults. The experimental results show that the maximum CMI algorithm and the dynamic MM significantly improve the estimation error of MSET. Compared with the recorded fault information, the proposed method realizes the fault early warning about 2–20 days in advance.