State evaluation of zinc oxide arresters based on the initial probabilistic self-learning Bayes algorithm

Improving the accuracy of state evaluation for Zinc Oxide (ZnO) surge arresters is essential for grid safety. This paper proposes a comprehensive state evaluation method based on an initial probabilistic self-learning Bayesian algorithm. The method firstly utilizes association rules to mine the correlations of state parameters under various fault modes. A five-level hierarchical criterion is established to quantify the health state, where thresholds and parameter scores are determined via fuzzy membership functions. By integrating historical, current, and predicted state information, the method performs self-learning on conditional probability tables to construct a Bayesian network assessment model. The conditional probability tables and transaction matrix heat maps for all state levels were obtained. Practical case verification indicates that this method achieves a condition assessment accuracy of 93.33%. Comparative analysis confirms that this performance is significantly superior to traditional single-parameter methods and clustering algorithms, validating the model's effectiveness in assessing equipment operation status.