Ant Colony Optimization of Rough Set for HV Bushings Fault Detection

TL;DR

This paper develops and compares machine learning models, including a novel combination using Ant Colony Optimization of Rough Set, to improve fault detection in transformer bushings, enhancing monitoring accuracy and speed.

Contribution

It introduces a hybrid approach combining Ant Colony Optimization with Rough Set theory for improved fault detection in transformer bushings.

Findings

The committee outperforms individual models in accuracy.

RBF trains the fastest among the models.

Rough Set achieves the highest classification accuracy.

Abstract

Most transformer failures are attributed to bushings failures. Hence it is necessary to monitor the condition of bushings. In this paper three methods are developed to monitor the condition of oil filled bushing. Multi-layer perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are developed and combined through majority voting to form a committee. The MLP performs better that the RBF and the RS is terms of classification accuracy. The RBF is the fasted to train. The committee performs better than the individual models. The diversity of models is measured to evaluate their similarity when used in the committee.