Fault Detection Method of Oil-immersed Transformer Based on Thermal Imaging

<p>In the power system, whether the transformer can work normally and stably will directly affect the safe operation of the power grid. Monitoring the real-time operational status of transformers is crucial for the early detection, diagnosis, and resolution of potential faults. In this paper, a fault detection method of oil-immersed transformer based on thermal imaging technology is proposed. Firstly, thermal imaging images of transformer under different working conditions are obtained by infrared thermal imaging technology. Then the feature extraction and fault detection of transformer thermal image are carried out by convolutional neural network. By conducting tests and validation on actual transformers, the accuracy of fault diagnosis has reached 98.7%, thus confirming the effectiveness and precision of this method.</p> <p> </p>

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Publication Details

Journal
電腦學刊
Published
2024-10-01
DOI
https://doi.org/10.53106/199115992024103505003
Citations
2
Primary Topic
Thermography and Photoacoustic Techniques
Type
article
Field-Weighted Citation Impact
0.54
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article

Fault Detection Method of Oil-immersed Transformer Based on Thermal Imaging

Chaochao Gao Shaorong Cao, Xueyan Song, Ming Song, Weidong Zhang et al.
2 citations
電腦學刊
Thermography and Photoacoustic Techniques
0.54
article

Fault Detection Method of Oil-immersed Transformer Based on Thermal Imaging

Chaochao Gao Shaorong Cao, Xueyan Song, Ming Song, Weidong Zhang, S Xie
article en
2 citations

Abstract

<p>In the power system, whether the transformer can work normally and stably will directly affect the safe operation of the power grid. Monitoring the real-time operational status of transformers is crucial for the early detection, diagnosis, and resolution of potential faults. In this paper, a fault detection method of oil-immersed transformer based on thermal imaging technology is proposed. Firstly, thermal imaging images of transformer under different working conditions are obtained by infrared thermal imaging technology. Then the feature extraction and fault detection of transformer thermal image are carried out by convolutional neural network. By conducting tests and validation on actual transformers, the accuracy of fault diagnosis has reached 98.7%, thus confirming the effectiveness and precision of this method.</p> <p> </p>

電腦學刊Vol. 35(5)
Openalex Percentile: Top 39%
Thermography and Photoacoustic Techniques
0.54
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