Wind driven generator bearing fault prediction method based on artificial neural network

Abstract

The invention relates to a wind generating set bearing fault prediction method based on a neural network, which relates to the field of wind generating set fault diagnosis. The method comprises the steps of: S1, collecting historical operation data of a wind driven generator bearing, carrying out data preprocessing, and carrying out the data standardization and data missing value filling of initial data, S2, carrying out frequency conversion by using improved stationary wavelet packet conversion so as to carry out frequency bandwidth separation, and extracting a fault characteristic frequencyvalue, S3, using an Elman artificial neural network and using the training set to train the Elman artificial neural network to obtain a neural network model, and S4, performing fault prediction on theinput real-time data. According to the method, the accuracy of wind driven generator bearing fault prediction is improved to a large extent, and the operation speed is obviously improved.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of wind power generating sets, and relates to a fault prediction method for wind power generator bearings based on artificial neural network. Background technique [0002] As a renewable and clean energy, wind energy has attracted worldwide attention because of its advantages such as low cost, large reserves, convenient use, cleanliness, safety and reliability. In recent years, related technologies of wind power generation have developed very rapidly. As the main way to utilize wind energy, wind power generation is one of the power generation methods with the most mature technology and the best development prospects in today's clean energy. The principle of wind power generation is to use the wind force to drive the blades of the windmill to rotate to promote the generator to generate electricity. [0003] Nowadays, the application scenarios of wind turbines are more and more extensive, from land ...

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Problems solved by technology

[0007] In view of this, the purpose of the present invention is to provide a wind turbine bearing fault prediction method based on artificial neural network, to solve the problem of low prediction accuracy, the need to add a new prediction device, and the high cost of wind turbine bearing fault prediction devices in the prior art. technical problems, aiming to provide neural network models to analyze real-time data, and then issue fault warnings, repair as soon as possible, and reduce economic losses

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