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| Partial discharge fault identification of switchgear based on convolutional neural network |
| Wang Feifei1, Ruan Aimin2, Wei Gang2, Sun Haibo2 |
1. Nanjing Institute of Technology, Nanjing 210000;
2. State Grid Zhenjiang Power Supply Company, Zhenjiang, Jiangsu 212000 |
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Abstract The current classification algorithms for partial discharge faults are mostly shallow learning algorithms, and the features extracted manually directly affect the classification results. In contrast to shallow learning algorithms, deep learning has a deeper architecture that automatically learns features from samples. Convolutional neural networks are typical deep learning algorithms. This thesis aims to study the application of convolutional neural network in partial discharge of switchgear, and proves that deep learning architecture can effectively improve the recognition rate. In this experiment, two kinds of audible signals are collected, which are normal and faulty. After extracting the above two types of sound signals, they are respectively classified into SVM model and convolutional neural network for classification. The experimental results show that the convolutional neural network improves the accuracy of voice recognition compared with the traditional SVM.
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Received: 05 September 2018
Published: 17 April 2019
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| Cite this article: |
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Wang Feifei,Ruan Aimin,Wei Gang等. Partial discharge fault identification of switchgear based on convolutional neural network[J]. Electrical Engineering, 2019, 20(4): 76-81.
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| URL: |
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http://dqjs.cesmedia.cn/EN/Y2019/V20/I4/76
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2019, Vol. 20 
