A Fault Identification Method of Wind Turbine Based on Hybrid Neural Network

Abstract

The invention discloses a fault identification method for a wind power generator based on a hybrid neural network, which specifically includes collecting time-domain waveform data of a gearbox, establishing original sample data, and labeling the data; extracting the minimum value of the amplitude and vibration speed in the waveform data and kurtosis index as features; input the extracted fault and normal eigenvalues ​​into the hybrid network 1D-CNN_Bi-GRU, the hybrid network connects 1D-CNN and Bi-GRU in series, and first uses 1D-CNN as the primary network to extract sequence local features , and then use the output of 1D-CNN as the input of Bi-GRU, and use the characteristics of Bi-GRU to obtain the cumulative dependency information from the past and the future from the forward direction at the same time, and further extract the long-term dependency features of the sequence for fault diagnosis ;Save the model, input the data to be analyzed into the model, and output the fault classification result.

Description

technical field [0001] The invention belongs to the technical field of fault identification for wind power generation, and relates to a key technical method for fault identification of a wind power generator based on a hybrid neural network. Background technique [0002] The field of wind power has developed rapidly in recent years, but the technology in the manufacture and maintenance of related equipment is still immature, and because the installation sites of wind power equipment are generally in relatively harsh environments, how to ensure that the wind power It has become a hot spot for technicians to be able to judge the hidden dangers of failures in advance through prediction. As the scale and cost of a single fan increase, the cost of maintenance also increases significantly. According to the results of the data, the service life of the general fan is about 20 years, and the daily maintenance and repair expenditure of the fan accounts for 10-15% of the total expendi...

AI Technical Summary

Problems solved by technology

[0012] Aiming at the problems of existing wind turbine fault diagnosis methods, such as difficult model establishment, general feature extraction ability, poor generalization ability, low precision and small data, a high-performance method based on bidirectional gated recurrent unit and one-dimensional convolutional neural network is proposed. Hybrid Neural Network Fault Diagnosis Method

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