Method and device for diagnosing faults of wind driven generator in dimension reduction mode

Abstract

The invention relates to a method and device for carrying out wind driven generator fault diagnosis in a dimension reduction mode. The method comprises the steps of constructing an original data spaceaccording to operation data of the wind driven generator in different operation states; performing feature selection on the sample data in the original data space by using a first mode to obtain a new data space; constructing a corresponding symmetric positive definite matrix manifold for any sample data in the new data space obtained based on the first mode by using a second mode; performing feature extraction on the symmetric positive definite matrix manifold of the sample data in a third mode to obtain a low-dimensional feature set of the sample data in the symmetric positive definite matrix manifold; and inputting the obtained low-dimensional feature set into a support vector machine, and detecting the fault of the wind driven generator according to the output information of the support vector machine. The fault detection precision of the wind driven generator can be improved.

Description

Technical field: [0001] The invention relates to a fault detection method and device for a wind power generator. Background technique: [0002] With the gradual improvement of manifold learning theory, manifold learning has been widely used in the field of fault diagnosis. Compared with the traditional feature extraction method, manifold learning takes the whole data set instead of a single data as the research object, and can make full use of the local structure information between the original data, but the application of manifold learning in fault diagnosis still exists. Many questions. It is specifically manifested in the fact that the existing manifold learning algorithm represents the sample data in the form of vectors, which ignores the structural information between the characteristics of the sample data; at the same time, representing the sample data in the form of vectors will cause the data "dimension disaster" problem; In addition, the existing manifold learnin...

AI Technical Summary

Problems solved by technology

Compared with the traditional feature extraction method, manifold learning takes the whole data set instead of a single data as the research object, and can make full use of the local structure information between the original data, but the application of manifold learning in fault diagnosis still exists. many problems

It is specifically manifested in the fact that the existing manifold learning algorithm represents the sample data in the form of vectors, which ignores the structural information between the characteristics of the sample data; at the same time, representing the sample data in the form of vectors will cause the data "dimension disaster" problem; In addition, the existing manifold learning algorithms all calculate the K nearest neighbor points of any sample data in the Euclidean space, which is contrary to the nonlinear characteristics of the manifold learning algorithm. There is a large error in this calculation method, which makes the final low Dimensional feature sets cannot accurately reflect the essential characteristics of the original data, which seriously affects the accuracy of fault diagnosis

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