基于批标准化的堆叠自编码网络风电机组变桨系统故障诊断

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

太阳能学报 ›› 2022, Vol. 43 ›› Issue (2): 394-401.DOI: 10.19912/j.0254-0096.tynxb.2020-0284

基于批标准化的堆叠自编码网络风电机组变桨系统故障诊断

王思华^1,2, 王恬¹, 周丽君¹, 王宇¹, 陈天宇¹, 赵珊鹏^1,2

1.兰州交通大学自动化与电气工程学院,兰州 730070;
2.甘肃省轨道交通电气自动化工程实验室,兰州 730070

收稿日期:2020-03-31 出版日期:2022-02-28 发布日期:2022-08-28
通讯作者: 王思华（1968—）,男,硕士、教授,主要从事高电压与绝缘技术的研究与教学工作。ws_h@163.com
基金资助:
国家自然科学基金（51767014; 51867013）

FAULT DIAGNOSIS OF PITCH SYSTEM OF WIND TURBINE BASED ON STANDARDIZED STACKED AUTOENCODER NETWORK

Wang Sihua^1,2, Wang Tian¹, Zhou Lijun¹, Wang Yu¹, Chen Tianyu¹, Zhao Shanpeng^1,2

1. College of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China;
2. Rail Transit Electrical Automation Engineering Laboratory of Gansu Province, Lanzhou 730070, China

Received:2020-03-31 Online:2022-02-28 Published:2022-08-28

摘要/Abstract

摘要： 为了提高风电机组变桨系统故障诊断的准确性,提出一种基于批标准化的堆叠自编码（SAE）网络故障诊断模型。针对SAE网络在特征学习过程出现的梯度硬饱和问题,选用PReLU激活函数,在SAE网络中加入批标准化（BN）层进行优化,通过输出层的Softmax函数,得到变桨系统各部件故障发生概率。以均方误差最小化为目标,采用Adam算法迭代训练数据,使模型参数得到更新。在风电机组变桨系统数据采集与监视控制（SCADA）系统中的数据集中,对优化前后的SAE网络通过改变迭代次数、样本数量进行实验,结果表明,优化后的SAE网络模型具有更好的识别精度;另外,在不同样本数量的实验中,与其他传统模型相比,优化后的SAE网络模型故障识别率也更高,表明其在风电机组故障诊断领域有一定的应用价值。

关键词: 风电机组, 变桨系统, 故障诊断, 批标准化, 堆叠自编码

Abstract: 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.

Key words: wind turbines, pitch system, fault diagnosis, batch normalization, stacking auto-encoder

中图分类号:

TK83

王思华, 王恬, 周丽君, 王宇, 陈天宇, 赵珊鹏. 基于批标准化的堆叠自编码网络风电机组变桨系统故障诊断[J]. 太阳能学报, 2022, 43(2): 394-401.

Wang Sihua, Wang Tian, Zhou Lijun, Wang Yu, Chen Tianyu, Zhao Shanpeng. FAULT DIAGNOSIS OF PITCH SYSTEM OF WIND TURBINE BASED ON STANDARDIZED STACKED AUTOENCODER NETWORK[J]. Acta Energiae Solaris Sinica, 2022, 43(2): 394-401.

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基于批标准化的堆叠自编码网络风电机组变桨系统故障诊断

FAULT DIAGNOSIS OF PITCH SYSTEM OF WIND TURBINE BASED ON STANDARDIZED STACKED AUTOENCODER NETWORK

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