Comparative Analysis of GPR and RBF Models for Predicting the Breakdown Voltage of Insulating Oils

Abstract

Accurately forecasting the breakdown voltage of insulating oils is a prerequisite for the reliable design and operation of high-voltage equipment. The present work focuses on developing data-driven artificial intelligence (AI) models to predict the breakdown voltage of transformer oil as a function of temperature and electrode spacing. Two different machine learning algorithms are applied and compared: Gaussian Process Regression (GPR) and Radial Basis Function (RBF) neural network. The experimental data for electrode distances of 5 mm and 20 mm are used to train, test, and validate the models using a 60/20/20 data-splitting scheme. The predictive capacity of the models is evaluated using the three metrics: mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R). Experimental results confirm that the model predictions are in excellent agreement with the measurements at short electrode distances for both models. Nevertheless, at longer distances, the differences between the two performances become quite substantial. The GPR method is more reliable and generalises better, particularly at 20 mm, where it yields lower validation errors than the RBF approach. In addition, as a probabilistic method, GPR enables the estimation of predictive uncertainty, which is essential for applications oriented toward safety and dependability. Overall, the present work has demonstrated GPR's capability to determine the breakdown voltage of insulating oils and its potential for high-voltage insulation diagnostics and design.