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<p>As a widely used fault detection equipment in distribution networks, the distribution network fault indicator plays an important role in timely fault judgment and power supply recovery. Therefore, when designing distribution network lines, the quality of distribution network fault indicators is an important factor affecting the fault judgment of the distribution network. This paper discloses a fault diagnosis method and system for the distribution network fault indicator based on the decision tree learning algorithm, which enhances the real historical data through generative adversarial network (GAN), generates the historical fault data of the distribution network fault indicator and the working operation status of each module of the fault indicator, and applies the decision tree algorithm to classify faults based on the enhanced data, achieving a diagnostic accuracy of 98%. The results show that the application of this method to the fault diagnosis of the distribution network can carry out automatic fault diagnosis and analysis, which greatly saves the labor cost of fault indicator maintenance.</p>

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Decision tree-based fault diagnosis system for distribution network fault indicators

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月5日

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  • Qingsheng Tian,
  • Bowen Zheng,
  • Hui Yang,
  • Shibin Liang,
  • Kaiguo Qian,
  • Shikai Shen,
  • Fei Deng

摘要

As a widely used fault detection equipment in distribution networks, the distribution network fault indicator plays an important role in timely fault judgment and power supply recovery. Therefore, when designing distribution network lines, the quality of distribution network fault indicators is an important factor affecting the fault judgment of the distribution network. This paper discloses a fault diagnosis method and system for the distribution network fault indicator based on the decision tree learning algorithm, which enhances the real historical data through generative adversarial network (GAN), generates the historical fault data of the distribution network fault indicator and the working operation status of each module of the fault indicator, and applies the decision tree algorithm to classify faults based on the enhanced data, achieving a diagnostic accuracy of 98%. The results show that the application of this method to the fault diagnosis of the distribution network can carry out automatic fault diagnosis and analysis, which greatly saves the labor cost of fault indicator maintenance.