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In order to improve the accuracy of state evaluation of zinc oxide arrester, a Bayesian network evaluation framework based on adaptive probabilistic learning mechanism is designed. According to the operation characteristics of zinc oxide arrester, the mapping relationship between fault mode and state parameters is established by multi-dimension correlation feature extraction technology. Then, a hierarchical criterion for quantifying the health status of lightning arrester is established by using the 5-level state evaluation method, and the corresponding equipment status is evaluated by using the historical, current and predicted state information of lightning arrester. Then, the conditional probability table self-learning is carried out by comprehensively considering the data characteristics of these three types of information and the correlation of state parameters. Finally, the zinc oxide state evaluation model based on Bayesian network is established. The comprehensive state evaluation method of equipment operation is given.

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State Evaluation of Zinc Oxide Arrester Based on Initial Probabilistic Self-learning Bayes Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Yuqing Wang,
  • Hongbin Zhang,
  • Yang Shen,
  • Zhitong Xue,
  • Xiaowei Huang,
  • Hongshun Liu

摘要

In order to improve the accuracy of state evaluation of zinc oxide arrester, a Bayesian network evaluation framework based on adaptive probabilistic learning mechanism is designed. According to the operation characteristics of zinc oxide arrester, the mapping relationship between fault mode and state parameters is established by multi-dimension correlation feature extraction technology. Then, a hierarchical criterion for quantifying the health status of lightning arrester is established by using the 5-level state evaluation method, and the corresponding equipment status is evaluated by using the historical, current and predicted state information of lightning arrester. Then, the conditional probability table self-learning is carried out by comprehensively considering the data characteristics of these three types of information and the correlation of state parameters. Finally, the zinc oxide state evaluation model based on Bayesian network is established. The comprehensive state evaluation method of equipment operation is given.