Health assessment of wind turbine based on laplacian eigenmaps

With the increasing complexity of wind turbines, the current situation of high failure rates and high maintenance costs has attracted the attention of wind power operators. The research on the health status monitoring of wind turbines is of great significance to the development of the wind power industry. In this study, a novel method for evaluating the health status of wind turbines is proposed. The method fully considers the characteristics of wind turbine health status with high-dimensional nonlinearity. Firstly, the Gaussian kernel density estimation Local Outlier Factor (GLOF) is used to clean the data. Secondly, feature parameters are extracted by Partial Least Squares (PLS). Finally, the dimension reduction method based on Laplacian Eigenmaps (LE) is used to map the processed wind turbine data, and the standard deviation of horizontal and vertical scales is used as the health condition evaluation model to evaluate the performance of the wind turbine. It was validated in a large onshore wind turbine dataset which collected three years of Supervisory Control And Data Acquisition (SCADA) system data. The results show that this method can stably monitor the health degradation of wind turbines and provide a theoretical basis for the staff to arrange the maintenance time of wind turbines reasonably.