基于孪生深度神经网络的风电机组故障诊断方法

doi:10.16182/j.issn1004731x.joss.21-0261

摘要/Abstract

摘要：

为有效提取风电SCADA (supervisory control and data acquisition)中时序数据故障特征，同时考虑一维卷积神经网络(one-dimensional, convolutional neural network，1-D CNN)提取局部时序特征和长短时记忆网络(long short-term memory networks, LSTM)提取长期依赖特征优势，提出一种基于1-D CNN-LSTM的风电机组故障诊断方法；针对故障样本稀缺问题，基于孪生神经网络架构(siamese network)，提出一种基于孪生深度神经网络siamese 1-D CNN-LSTM的风电机组故障诊断方法，依靠少量样本数据对机组故障特征进行有效提取。结果表明：1-D CNN-LSTM优于其他现有深度学习方法；当训练样本不足时，所提出的siamese 1-D CNN-LSTM可以显著提升故障诊断结果。

关键词: 风电机组, 故障诊断, 深度学习, 孪生神经网络, 少样本学习

Abstract:

In order to effectively extract the fault features of time series data in supervisory control and data acquisition (SCADA), considering the advantages of one-dimensional convolutional neural network (1-D CNN) for extracting local time series features and the advantages of long-term memory (LSTM) which can extract long-term dependent features, a method for fault diagnosis of wind turbines based on 1-D CNN-LSTM is proposed. To solve the problem of the scarcity of fault samples of wind turbines based on the siamese network architecture, a wind fault diagnosis method based on siamese 1-D CNN-LSTM is proposed. The proposed siamese 1-D CNN-LSTM method relies on a small amount of sample data to effectively extract the fault features of the wind turbine. The results show that 1-D CNN-LSTM is better than other existing deep learning methods. When the training samples are insufficient, the proposed siamese 1-D CNN-LSTM can significantly improve the fault diagnosis results.

Key words: wind turbine, fault diagnosis, deep learning, siamese neural network, few-shot learning

中图分类号:

TM315
TP391

刘家瑞, 杨国田, 王孝伟. 基于孪生深度神经网络的风电机组故障诊断方法[J]. 系统仿真学报, 2022, 34(11): 2348-2358.

Jiarui Liu, Guotian Yang, Xiaowei Wang. A Wind Turbine Fault Diagnosis Method Based on Siamese Deep Neural Network[J]. Journal of System Simulation, 2022, 34(11): 2348-2358.

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参数	取值	参数	取值
卷积层1	1×5@64, S=1	softmax	Nf
池化层1	1×2, S=2	激活函数	ReLU
卷积层2	1×3@32, S=1	batch size	32
池化层2	1×2, S=2	迭代次数	50
LSTM	64	优化器	Adam
全连接层	80	学习率	0.01
drop out	0.3

数据集A	数据集B
风速(m·s^-1)	风速(m·s^-1)
桨距角(º)	桨距角(º)
偏航位置(º)	内部功率(kW)
网侧功率(kW)	功率因数(%)
无功功率(var)	频率(Hz)
发电机转速(rpm)	发电机电压(V)
环境温度(℃)	发电机输出(kW)
机舱温度(℃)	发电机转速(rpm)
发电机转子温度(℃)	齿轮箱轴承温度(℃)

数据集	数据	训练集(70%)	测试集(30%)	子训练集1(5%)(支撑集)	子训练集2(1%)(支撑集)
A	1 261 (正常：631，结冰：630)	883	378	57 (114)	11 (22)
B	10 267 (轴承异常：1 768，通地泄露故障：2 483，叶片损坏：2 618，发电机异常：3 398)	7 181	3 086	359 (718)	71 (142)

数据集	模型	Precision	Recall	F1score	Accuracy
A	LSTM	0.837±0.030 9	0.871±0.025 9	0.853±0.038 7	0.858±0.035 3
	1-D CNN	0.912±0.018 7	0.904±0.026 8	0.904±0.025 1	0.908±0.025 6
	1-D CNN-LSTM	0.922±0.009 2	0.925±0.012 4	0.921±0.016 0	0.923±0.010 1
B	LSTM	0.951±0.014 1	0.934±0.019 4	0.938±0.016 4	0.947±0.023 5
	1-D CNN	0.974±0.008 9	0.966±0.005 9	0.968±0.007 1	0.969±0.008 9
	1-D CNN-LSTM	0.979±0.005 5	0.971±0.003 7	0.974±0.004 1	0.986±0.002 7