A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model

TLDR

State‑parameter correlation analysis is essential for safe and efficient transformer operation, yet current methods rely on only a few parameters and often miss potential failures. The study proposes a data‑driven association‑rule mining approach that combines the Apriori algorithm with a probabilistic graphical model to analyze transformer state parameters. The method applies Apriori‑based frequent‑item mining within a probabilistic graphical model to extract association rules from numerical solutions of differential equations and transformer measurements, thereby eliminating cyclic data scans and enabling rule‑based refinement of single‑parameter predictions. The approach successfully mines accurate association rules from numerical solutions and transformer data, and applying these rules improves prediction accuracy, demonstrating the method’s effectiveness and feasibility.

Abstract

The correlative change analysis of state parameters can provide powerful technical supports for safe, reliable, and high-efficient operation of the power transformers. However, the analysis methods are primarily based on a single or a few state parameters, and hence the potential failures can hardly be found and predicted. In this paper, a data-driven method of association rule mining for transformer state parameters has been proposed by combining the Apriori algorithm and probabilistic graphical model. In this method the disadvantage that whenever the frequent items are searched the whole data items have to be scanned cyclically has been overcame. This method is used in mining association rules of the numerical solutions of differential equations. The result indicates that association rules among the numerical solutions can be accurately mined. Finally, practical measured data of five 500 kV transformers is analyzed by the proposed method. The association rules of various state parameters have been excavated, and then the mined association rules are used in modifying the prediction results of single state parameters. The results indicate that the application of the mined association rules improves the accuracy of prediction. Therefore, the effectiveness and feasibility of the proposed method in association rule mining has been proved.