Optimizing Artificial Neural Networks with Particle Swarm Optimization for Accurate Prediction of Insulator Flashover Voltage Under Dry and Rainy Conditions

Abstract

Outdoor insulators are highly susceptible to environmental factors, such as moisture, rain, and contaminants, which significantly degrade their efficiency and durability. These factors contribute to surface flashovers, leading to insulation failures in outdoor power systems. This study presents a novel application of advanced machine learning techniques to predict the flashover performance of glass insulators under diverse environmental conditions, focusing on dry and rainy scenarios. The research emphasizes the critical role of raindrops in reducing flashover voltage. A hybrid model combining Artificial Neural Networks (ANN) with Particle Swarm Optimization (PSO) is developed to address these challenges. The PSO algorithm optimizes the ANN's hyperparameters, enabling the model to establish a nonlinear relationship between key insulator characteristics, including standard and anti-pollution profiles and their critical flashover voltage. Rigorous testing demonstrated exceptional accuracy, with a mean absolute percentage error (MAPE) of 0.2458 and a near-perfect coefficient of determination (R²) of 0.999. These findings highlight the robustness and reliability of the proposed hybrid model in predicting flashover voltage under varying environmental conditions. This work provides a powerful tool for enhancing the design, maintenance, and operational reliability of outdoor insulators, particularly in regions prone to high levels of pollution and moisture, contributing significantly to the advancement of sustainable power transmission systems.