Identification Winding Material of Distribution Transformer Based on Multi-Information Fusion

In transformer production, some manufacturers use aluminum to impersonate copper to reduce the manufacturing cost, which is difficult to be detected and can cause significant losses to the power grid. To address the problem of non-destructive identification of winding material in distribution transformers, we propose a multi- information recognition method by fusing the harmonic resistance coefficient and the appearance parameters of the transformer, namely volume and height. Then the winding material is identified by a trained support vector machine model. The test results demonstrate that our proposed method achieves 90% recognition accuracy for copper transformers and 100% accuracy for aluminum transformers with the test samples. Additionally, the proposed non-destructive method is easier to implement than other methods in engineering.

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Publication Details

Journal
IEEE Transactions on Applied Superconductivity
Published
2024-09-10
DOI
https://doi.org/10.1109/tasc.2024.3456559
Citations
2
Primary Topic
Smart Grid and Power Systems
Type
article
Field-Weighted Citation Impact
0.42
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article

Identification Winding Material of Distribution Transformer Based on Multi-Information Fusion

Xianming Xie, Zhixiong Liu, Hu Xiong, Bin Xiang et al.
2 citations
IEEE Transactions on Applied Superconductivity
Smart Grid and Power Systems
0.42
article

Identification Winding Material of Distribution Transformer Based on Multi-Information Fusion

Xianming Xie, Zhixiong Liu, Hu Xiong, Bin Xiang, Xiaoguang Jiang, Jiayuan Li, Changchen Zhu
article en
2 citations

Abstract

In transformer production, some manufacturers use aluminum to impersonate copper to reduce the manufacturing cost, which is difficult to be detected and can cause significant losses to the power grid. To address the problem of non-destructive identification of winding material in distribution transformers, we propose a multi- information recognition method by fusing the harmonic resistance coefficient and the appearance parameters of the transformer, namely volume and height. Then the winding material is identified by a trained support vector machine model. The test results demonstrate that our proposed method achieves 90% recognition accuracy for copper transformers and 100% accuracy for aluminum transformers with the test samples. Additionally, the proposed non-destructive method is easier to implement than other methods in engineering.

IEEE Transactions on Applied SuperconductivityVol. 34(8)
State Grid Corporation of China (China) (CN), Wuhan University (CN), State Grid Hebei Electric Power Company
Affordable and clean energy
Openalex Percentile: Top 38%
Smart Grid and Power Systems
0.42
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