KNOWLEDGE EXTRACTION TECHNIQUES FOR POWER TRANSFORMER MAINTENANCE DATA: REVIEW

Abstract

The maintenance of power transformers is time or condition-based and at the end of this one, analysis reports are produced to give its status. These different reports produced over time form a large database called the transformer maintenance asset bank. Extracting knowledge from this power transformer maintenance data is now an important subject for the scientific community given the importance of the transformer in the electric power generation chain. The science of data mining finds a field of application for its analysis techniques, the most used in preventive maintenance are predictive techniques. This work reviews knowledge extraction techniques from power transformer maintenance data. For this purpose, 80 articles from a platform are identified and 7 of them are retained at the end after meeting the criteria. Among the predictive analysis techniques namely regression, classification, and prediction, classification is the most used with its ANN (Artificial Neural Network) algorithm. On the other hand, association rule mining (ARM) has the highest accuracy, 98.21% in 2020. In addition, the combination of a classification algorithm preceded by the descriptive one, namely the principal component analysis (PCA), could offer higher accuracy than when they are used individually.