Fault Identification Method for Newly Added Categories in High Voltage Circuit Breakers Based on Improved Transfer Learning

WANG Xinghua; YANG Haowen; MA Yulin; ZHAO Junsheng

doi:10.13296/j.1001-1609.hva.2025.10.012

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Fault Identification Method for Newly Added Categories in High Voltage Circuit Breakers Based on Improved Transfer Learning

Digital Twin Technology and Applications for Multi-scenario in New Power System | 更新时间：2025-10-21

- Fault Identification Method for Newly Added Categories in High Voltage Circuit Breakers Based on Improved Transfer Learning
- High Voltage Apparatus Vol. 61, Issue 10, Pages: 106-116(2025)
- 作者机构：
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- DOI：10.13296/j.1001-1609.hva.2025.10.012
  CLC：
- Received：11 March 2025，
  
  Revised：2025-06-07，
  
  Published：16 October 2025
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WANG Xinghua, YANG Haowen, MA Yulin, et al. Fault Identification Method for Newly Added Categories in High Voltage Circuit Breakers Based on Improved Transfer Learning[J]. High voltage apparatus, 2025, 61(10): 106-116.
DOI：

WANG Xinghua, YANG Haowen, MA Yulin, et al. Fault Identification Method for Newly Added Categories in High Voltage Circuit Breakers Based on Improved Transfer Learning[J]. High voltage apparatus, 2025, 61(10): 106-116. DOI： 10.13296/j.1001-1609.hva.2025.10.012.

摘要

针对实际高压断路器故障发生的时空不确定性和暂态过程的随机性引发新增类别的未知故障类型，且其样本分布呈现长尾特性，导致现有故障识别模型出现误判的问题，提出了一种基于改进迁移学习的高压断路器新增类别故障精准识别方法。首先，提取实际在运高压断路器振动信号的多维特征参数，构建具有强特征提取能力的故障识别模型。其次，通过计算Mahalanobis距离和置信度辨识新增类别的未知故障。最后，采用伪标签和迁移学习将长尾分布的未知故障类别纳入模型训练，更新后的模型能够准确预测运行数据的故障类型。结果表明，改进后的诊断模型平均准确率达到97.83%，相比于现有特征迁移方法的准确率平均提升了40.22%。

Abstract

In view of the issues of misjudgment in existing fault identification models due to the spatiotemporal uncertainty and randomness of the transient process of actual high voltage circuit breaker fault

which lead to the emergence of new unknown fault types and the long-tail distribution of their samples

a precise identification method for new category fault of high voltage circuti breaker based on improved transfer learning is proposed . Frist

the multidimensional feature parameters of the vibration signals of the actual operating high-voltage circuit breakers are extracted to construct a fault identification model with strong feature extraction capability. Then

the unknown faults of the newly added categories are identified by calculating Mahalanobis distance and confidence level. Finally

pseudo-labeling and migration learning are used to incorporate the unknown fault categories with long-tailed distributions into the model training

and the updated model can be able to accurately predict the fault types of operational data. The results show that the average accuracy of the improved diagnostic model is upto 97.83%

which is an average improvement of 40.22%compared to the accuracy of existing feature transfer methods.

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Keywords

references

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