Advanced Fusion of MLP, RBF, and SVR Models for Predicting Short Gap Breakdown Voltage in Air Gaps under varying Temperature and Humidity conditions

Air gaps are the principal insulating medium for transmission lines. The atmospheric conditions have an important nonlinear impact on the breakdown voltage prediction, such as temperature and humidity. Unlike previous studies that use a single model, this paper introduces a weighted fusion approach, which has not previously been used to test the air gap insulation performance. This novel strategy assigns different importance levels to each model based on its strengths. The experimental breakdown voltage for plane-plane electrodes air gap is in good agreement with the estimated breakdown voltage using three neural network models: Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Regression (SVR). The performance of these models is measured using error metrics such as MSE, MAPE, MSPE, and RMSE, with the RBF model showing the best accuracy, reaching MSE values of 1.4139% for humidity and 1.3855% for temperature. This fusion method significantly improves prediction accuracy, reducing MSE to 0.5378% for humidity and 0.6268% for temperature. The results confirm that the proposed approach enhances prediction reliability and helps improve insulation performance in power systems.