Monitoring of Outdoor Insulators under Heavily Polluted Conditions using IoT

This research investigates the application of machine learning techniques, specifically neural networks, to estimate the critical voltage of flashover in external insulators. The proposed method utilizes experimental data and theoretical models to train and validate the neural network. By analyzing factors such as environmental conditions, insulator design, and operating parameters, the model can predict the critical flashover voltage with high accuracy. The integration of IoT technology enables real-time monitoring of insulator conditions, facilitating timely maintenance and reducing the risk of power outages. This research contributes to the advancement of power system reliability and enhances the safety and efficiency of electrical grids.