Fault diagnosis of pitch system of wind turbine based on standardized stacked autoencoder network

被引：0

作者：

Wang S. ^{[1
,2
]}

Wang T. ^{[1
]}

Zhou L. ^{[1
]}

Wang Y. ^{[1
]}

Chen T. ^{[1
]}

Zhao S. ^{[1
,2
]}

机构：

[1] College of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou

[2] Rail Transit Electrical Automation Engineering Laboratory of Gansu Province, Lanzhou

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 02期

关键词：

Batch normalization; Fault diagnosis; Pitch system; Stacking auto-encoder; Wind turbines;

D O I：

10.19912/j.0254-0096.tynxb.2020-0284

中图分类号：

学科分类号：

摘要：

In order to improve the accuracy of fault diagnosis of wind turbine pitch system, a fault diagnosis model based on batch normalization of stacked auto-encode (SAE) network is proposed. Aiming at the problem of hard gradient saturation in the feature learning process of the SAE network, the PReLU activation function is selected, and the batch normalization (BN) layer is added to the SAE network for optimization. Through the Softmax function of the output layer, the failure probability of each component of the pitch system is obtained. With the goal of minimizing the mean square error, the Adam algorithm is used to iterate the training data to update the model parameters. In the data set of wind turbine pitch system supervisory control and data acquisition (SCADA) system, the SAE network before and after optimization is tested by changing the number of iterations and the number of samples. The results show that the optimized SAE network model has better recognition accuracy. In addition, in the experiments with different sample numbers, compared with other traditional models, the fault recognition rate of the optimized SAE network model is also higher, indicating that it has certain application value in the field of wind turbine fault diagnosis. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.

引用

页码：394 / 401

页数：7

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