小样本下基于CNN-DCGAN的电缆局部放电模式识别方法

doi:10.16180/j.cnki.issn1007-7820.2022.07.002

Abstract

Abstract:

In the process of cable partial discharge pattern recognition, the traditional manual feature extraction relies on the knowledge and experience of specific fields, and the workload of feature selection and optimization is heavy. In view of this problem and to avoid the overfitting problem of the classifier under the unbalanced small sample data of the model, this study presents a partial discharge pattern recognition method based on CNN-DCGAN in the case of small samples. Partial discharge time domain signals are transformed into two-dimensional image information by sliding time window. The DCGANs are constructed, and the data enhancement is carried out on the basis of the original data set. The original data and the enhanced data are taken as the system input. CNN is constructed, and its nonlinear encoder is used to automatically extract partial discharge features, and the feature classification model is trained by Softmax layer. Experimental results show that compared with artificial features, the recognition accuracy of CNN classifier based on automatic feature extraction is improved by 4.18%. Compared with the original data set, the system recognition accuracy based on the sample enhanced data set is improved by 3.175%.

Key words: partial discharge, feature extraction, data augmentation, convolution neural networks, generative adversarial networks, pattern recognition, insulation defect, time domain signal

CLC Number:

TP181

SUN Kang,XUAN Xuyang,LIU Penghui,ZHAO Laijun,LONG Jie. Partial Discharge Pattern Recognition of Cable Based on CNN-DCGAN under Small Data[J].Electronic Science and Technology, 2022, 35(7): 7-13.

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References 21

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缺陷类型	击穿电压/kV	实验电压/kV
外导电层爬电	7.7	5.6
绝缘内部气隙	25.0	5.3
绝缘表面划伤	17.0	5.6
绝缘表面金属污秽	51.5	20.0

网络层	大小
Input	64×64二维图像
Conv1	3×3×16
Pool1	3×3×16
Conv2	3×3×32
Pool2	3×3×32
Conv3	3×3×64
Pool3	3×3×64
FC	128

训练数据集	测试数据集	识别精度
D₁	D₂	93.425%
D₁+D₃	D₂	96.600%

Partial Discharge Pattern Recognition of Cable Based on CNN-DCGAN under Small Data

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缺陷类型		外导电层爬电	绝缘内部气隙	绝缘表面划伤	绝缘表面金属污秽
标签		0	1	2	3
样本集D₁	Train	100	100	100	100
样本集D₂	Test	100	100	100	100
样本集D₃		100	100	100	100

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