Wind turbine generator fault diagnosis method

Abstract

The invention discloses a wind turbine generator fault diagnosis method, which comprises the following steps: according to the vibration signal characteristics of a wind turbine generator gearbox, carrying out variational mode decomposition on signals under different working conditions to obtain a series of intrinsic mode function components, and respectively solving multi-scale permutation entropies of the intrinsic mode function components; combining the multi-scale permutation entropy and the original signal time domain feature into a feature vector, and inputting the feature vector into atransfer learning algorithm; the covariance of a source domain and a target domain being minimized through a linear transformation matrix, the distribution difference of signal data of the source domain and the target domain being reduced through second-order statistics alignment, and then inputting the feature vectors of the aligned signal data of the source domain and the target domain into a support vector machine for fault classification. According to the method, the problem of poor classification effect caused by different distribution of the vibration signal data under different workingconditions can be solved, and the method has higher accuracy in wind turbine generator fault diagnosis under variable working conditions.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a fault diagnosis method for wind turbines based on alignment of VMD-MPE and second-order statistics, which is used for feature extraction and classification of wind turbine faults under multiple working conditions. Background technique [0002] The number of installed wind turbines is increasing year by year, and they are often located in harsh environments such as deserts and mountains. Therefore, the health status monitoring of wind turbines has attracted widespread attention. A wind turbine gearbox is one of the most important and fragile components. In particular, the transmission structure is complex and prone to wear, cracks and other failures. It is difficult to obtain a large number of labeled vibration signals in harsh environments, and the measured vibration signals are non-Gaussian and non-stationary, resulting in low accuracy of fault diagnosis...

AI Technical Summary

Problems solved by technology

[0004] Purpose of the invention: The present invention provides a fault diagnosis method for wind turbines, which can solve the problems of not being able to mine the deep correlation of vibration signal characteristics between different working conditions and extracting fault signals. The characteristics of strong characterization ability, and the problem of poor classification effect due to the different distribution of vibration signal data under different working conditions

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