变压器油
变压器
故障检测与隔离
材料科学
石油工程
计算机科学
工程类
电气工程
人工智能
执行机构
电压
作者
Xueyan Song,Ming Song,Weidong Zhang,S Xie,Chaochao Gao Shaorong Cao
出处
期刊:Diannao xuekan
[Angle Publishing Co., Ltd.]
日期:2024-10-01
卷期号:35 (5): 035-046
被引量:2
标识
DOI:10.53106/199115992024103505003
摘要
<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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