Application of Convolutional Neural Networks in Pattern Recognition of Partial Discharge Image

被引：0
作者：
Wan X. [1 ]
Song H. [1 ]
Luo L. [1 ]
Li Z. [1 ]
Sheng G. [1 ]
Jiang X. [1 ]
机构：
[1] Department of Electrical Engineering, Shanghai Jiao Tong University, Minhang District, Shanghai
来源：
Dianwang Jishu/Power System Technology | 2019年 / 43卷 / 06期
关键词：
Convolutional neural network (CNN); Image; Partial discharge; Pattern recognition;
D O I：
10.13335/j.1000-3673.pst.2018.1345
中图分类号：
学科分类号：
摘要：
With established big data platforms, a large number of unstructured on-site data such as images are accumulated in data centers. Traditional partial discharge pattern recognition method is generally aimed at structured data and can not be directly applied to unstructured data. To solve this problem, a time-domain waveform pattern recognition method based on one-dimensional convolutional neural network is proposed. The image processing technology is used to preprocess the input images and one-dimensional characteristics of the waveform are obtained. Then the linearized function is used to normalize the data. Based on deep learning, the network is used for pattern recognition directly. Through on-site detection and lab simulated experiments, image data sets for five partial discharge defects are established and comparative experiments are conducted. Experimental results show that the recognition rate of partial discharges using one-dimensional convolutional neural network is 88.9%, significantly higher than that of support vector machine and back propagation neural network model. It also performs better than two-dimensional convolutional neural network under the same time complexity. The method autonomously learns features through the network and does not need to manually extract features. In conclusion, it has advantages of lower experimental complexity, higher recognition rate and better robustness. © 2019, Power System Technology Press. All right reserved.
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页码：2219 / 2226
页数：7
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