A Hybrid Regression Model to Estimate Remaining Useful Life of Transformer Liquid

The prediction of the remaining useful life (RUL) of transformer oil helps in condition monitoring and health monitoring of oil-filled power transformers. However, the prediction of RUL depends on the aging condition of the insulation system. In this article, a novel hybrid machine learning (ML)-based regression model is developed for predicting the RUL of the insulating oil in years. A total of 26 features have been taken from different chemical and physical properties and indices of mineral oil. Later, features are selected using the Pearson correlation coefficient (
$P_{c}$
) and conditional mutual information-based feature selection (CMIFS) techniques. Finally, a hybrid algorithm consisting of support vector regression (SVR), k-nearest neighbor (k-NN), multiple layer perceptron (MLP), ridge regression (RR), ElasticNet, adaptive boosting (AdaBoost), and extreme gradient boost (XGBoost) are used to predict the RUL of the oil. The performance of the hybrid model is analyzed by root mean square error (RMSE), root mean square logarithmic error (RMSLE), mean absolute error (MAE), and correlation coefficient (
${R}^{{2}}$
). The comparison with the individual base regression algorithm showed that the hybrid model performed better. This study adds to the arguments that data-driven intelligent monitoring systems are essential for the safe and efficient health monitoring of transformers.