A transformer fault diagnosis method based on multiscale 1DCNN

doi:10.19781/j.issn.1673-9140.2023.04.011

Home > Archive>Volume 38, Issue 4, 2023 >104-112. DOI:10.19781/j.issn.1673-9140.2023.04.011

A transformer fault diagnosis method based on multiscale 1DCNN
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                        10.19781/j.issn.1673-9140.2023.04.011
                    
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                        WANG Huidong1WANG Huidong
Yuhang District Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311100, China
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YAO Haiyan1YAO Haiyan
Yuhang District Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311100, China
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GUO Qiang2GUO Qiang
Yuhang Qunli Complete Electric Manufacturing Branch, Hangzhou Electric Power Equipment Manufacturing Co., Ltd.,Hangzhou 311100, China
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YU Xiaoling1YU Xiaoling
Yuhang District Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311100, China
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ZHANG Xufeng2ZHANG Xufeng
Yuhang Qunli Complete Electric Manufacturing Branch, Hangzhou Electric Power Equipment Manufacturing Co., Ltd.,Hangzhou 311100, China
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CONG Longkun3CONG Longkun
School of Mechanical Engineering，Northest Electric Power University, Jilin 132011, China
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Affiliation:(1. Yuhang District Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311100, China;2. Yuhang Qunli Complete Electric Manufacturing Branch, Hangzhou Electric Power Equipment Manufacturing Co., Ltd.,Hangzhou 311100, China;3.School of Mechanical Engineering，Northest Electric Power University, Jilin 132011, China)
Clc Number:TM411
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Abstract:

In order to improve the diagnostic accuracy of transformer fault, a transformer fault diagnosis method based on the multi?scale convolutional neural network model is proposed. Firstly, two multi?scale convolutional modules are designed on the basis of the 1DCNN structure, and the overall structure of the transformer fault identification model is constructed. Secondly, to handle the problem of less sample features, the feature expansion method based on the ratio method is adopted to enhance the sample features from 5 dimensions to 25 dimensions. To solve the small sample size of faults and uneven distribution of sample numbers between faults, a sample number enhancement method based on adversarial generation network is adopted, and a large number of simulated samples are generated. Finally, the modified dataset was used to train and test the designed model. The results show that the average accuracy of the model is 93.24%, and the model performs well compared with the relevant mainstream methods under different datasets.

Key words:transformer;fault diagnosis;dissolved gas in oil;ratio method;multi‑scale convolutional neural networks;antagonistic generation network

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王辉东,姚海燕,郭强,俞啸玲,张旭峰,丛龙坤.基于多尺度卷积神经网络的变压器故障诊断方法[J].电力科学与技术学报英文版,2023,38(4):104-112. WANG Huidong, YAO Haiyan, GUO Qiang, YU Xiaoling, ZHANG Xufeng, CONG Longkun. A transformer fault diagnosis method based on multiscale 1DCNN[J]. Journal of Electric Power Science and Technology,2023,38(4):104-112.

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