Bearing temperature model based wind turbine fault prediction method

Abstract

The invention discloses a bearing temperature model based wind turbine fault prediction method. The method includes the following steps: 1) selecting a bearing according to a fault monitoring target of wind turbines; 2) analyzing SCADA operation data, and selecting modeling parameters of a bearing temperature model by using principal component analysis; 3) establishing an LRRBF prediction model ofbearing temperature in healthy state according to historical healthy state operation data on the basis of a radial basis function neural network and a linear regression analysis method; 4) calculating a predicted value of the bearing temperature in an actual operation state according to the present operation data on the basis of the LRRBF prediction model; and 5) calculating a residual error between the predicted value of the bearing temperature and an actual operation value, and analyzing the residual error by using a sliding window method. If a mean value of the residual error exceeds a preset confidence interval, the fault monitoring target is judged to have a fault, and so the fault prediction of the wind turbines is realized. The fault of the wind turbines is predicted through the bearing temperature, which is economical and efficient.

Description

technical field [0001] The invention relates to a fault prediction method for a wind turbine, in particular to a fault prediction method for a wind turbine based on a bearing temperature model, and belongs to the technical field of state monitoring and fault diagnosis. Background technique [0002] With the progress of the times and the improvement of human awareness of environmental protection, the development and utilization of renewable clean energy has attracted more and more attention from the international community. As one of the new energy sources with the most mature technology and the greatest potential for large-scale commercial development in the world, wind energy has the characteristics of rich reserves, renewable, wide distribution, and no pollution, and has the value of large-scale development and utilization. As a clean energy with abundant reserves, renewable and zero emissions, wind power technology has become an important field for the development of vari...

AI Technical Summary

Problems solved by technology

Therefore, the gearbox and generator of wind turbines are components with a high failure rate. The state monitoring of the existing technology still has insufficient predictive ability in advance, and the operating status of key components cannot be effectively monitored.

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