Fault analysis and evaluation of power transformers based on family defects

Abstract

Family defects in transformers are equipment defects caused by common factors such as design, material, and manufacturing process. This article conducts data discretization and other processing on the collected data, and improves the original Apriori algorithm to establish a family defect model. By analyzing the correlation between various indicators of family defects and fault feature quantities through the block array Apriori algorithm, it is found that each defect indicator reflects the state of the power transformer. Evaluate association rules for high confidence problems, and then use family defect scoring criteria to evaluate family defects, and judge the impact of power transformer status based on the scores.