A General Methodology for Evaluation and Classification of Oil-Immersed Power Transformers: Application to Electrical and Physicochemical Parameters

Abstract

Condition monitoring of power transformers is of vital importance to prevent electricity supply stoppages and reduce power plant maintenance costs. To that end, the use of techniques to evaluate and classify the condition of these devices is highly recommended in order to obtain good quality information for their proper maintenance planning. This article presents and details a general methodology for the creation of methods to evaluate and classify these devices, by means of computational modeling and optimization. The results indicate a higher than 93% accuracy rate compared to that of numerical evaluations and symbolic classifications expected by experts, thus demonstrating the applicability of the proposed methodology, which is found to be superior in comparisons against Computational Intelligence and Statistical Learning methods.