State identification and fault early warning of wind turbine based on MSET

In recent years, with the development of wind power industry, the installed capacity is increasing rapidly, and the installation sites are developing towards the ocean and remote mountainous areas, which makes the maintenance of wind turbines difficult and the cost increases. In order to reduce the maintenance cost and improve the maintenance efficiency, and ensure the economic and reliable operation of the equipment, many state recognition and fault early warning methods have been proposed one after another, and the intelligent fault diagnosis method based on supervisory control and data acquisition(SCADA) data and various kinds of machine learning has gradually become a research hotspot. In this paper, the generator system of a wind turbine in a wind farm is taken as the research object. By using the massive SCADA data recorded in operation, a multivariate state estimation technique (MSET) is established to predict specific operation parameters with relevant operation parameters as input. This method uses clustering algorithm to clean up the selected SCADA data, then uses MSET to establish the prediction model, and calculates the prediction residual by sliding window method to realize the fault diagnosis. Finally, the effectiveness of the method is verified by actual SCADA data.