# State Evaluation of Electrical Equipment in Substations Based on Data Mining
## 1 Introduction
The study employs graph computing to construct an automatic defect identification model for major substation equipment, including transformers and circuit breakers. This model integrates multisource heterogeneous data to assess defect susceptibility across equipment from various manufacturers and production batches. A condition cleaning algorithm eliminates duplicate and false combinations, improving defect detection accuracy and efficiency. Association rule mining uncovers potential key relationships between database attributes. The a priori algorithm, a prominent association rule method, is widely applied due to its simplicity and efficiency in processing large datasets [12]. However, the basic a priori algorithm often struggles to mine rules of user interest effectively [13; 14]. The a priori algorithm typically relies on discretization or frequency thresholding methods when calculating frequent itemsets--it may overlook low-frequency rules with significant correlations [15]. The frequent itemset definition in the a priori algorithm's rule mining process may result in key rules omission. Recent research proposes methods to improve the a priori algorithm's handling of continuous data. Combining K-means clustering with the a priori algorithm enhances its applicability and accuracy for continuous data through discretization [16]. Researchers propose using probability distributions for continuous data processing, improving association rule reliability and statistical significance [17]. This approach emphasizes probabilistic modeling in data mining for more accurate and practical rules. The improved a priori algorithm shows considerable promise in improving the accuracy of electrical equipment state assessments. Further research is warranted to analyze the integration of the enhanced a priori algorithm with the Naive Bayes classifier. The Naive Bayes classifier, renowned for its computational efficiency and simplicity in classification tasks, presents potential enhancements for state evaluation models [18]. The integration of these two methods can enhance the identification of equipment status characteristics while maintaining the detection of crucial rules. The amalgamation of the a priori algorithm and Naive Bayes classifier presents a novel approach to tackling the aforementioned challenges. The Naive Bayes classifier, known for its simplicity and efficiency, effectively mitigates the limitations of the traditional a priori algorithm in classification tasks when integrated [19]. This integrated model facilitates both association rule mining and comprehensive rule classification and analysis, substantially improving the accuracy and reliability of state evaluation. Table 1 summarizes the latest trends in electrical equipment state evaluation and data mining research. This paper proposes a data mining-based method for assessing the state of electrical equipment in smart substations. It aims to evaluate the reliability of diverse electrical equipment by analyzing historical and real-time operational data, using a combination of improved association rule algorithms and Naive Bayes classifiers. The method consists of three primary phases: data preprocessing, association rule mining, and equipment classification. A Gaussian mixture model is employed to fit the original dataset, with the expectation-maximization algorithm to summarize data distribution characteristics and filtering outliers. The a priori algorithm is applied to the preprocessed dataset to identify frequent itemsets and derive association rules, which are then used for model training. For new equipment samples, conditional probabilities of each feature are independently calculated and combined. This process enables the evaluation of electrical equipment status. The scheme achieves high computational efficiency by determining crucial operating characteristics of electrical equipment (such as insulation resistance, contact resistance, mechanical properties) and identifying their influence on equipment failure.