Utilisation of AI to Predict Corrosion and Tear of Electrical Equipment in Distributing Networks

This research explores the incorporation of AI into predicting corrosion and tear in electrical equipment within distributing networks. AI leverages historical and real-time data to anticipate remaining useful life and detect potential failures before they happen. Innovations like cloud computing facilitate real-time data analysis and improve decision-making, while IoT sensors enable continuous monitoring. The study examines the uses and efficiency of IoT usage alongside the other technologies to further enhance communications between devices to provide a reliable and great system capable of detecting and predicting anomalies. This research concludes that AI will contribute a significant role in reducing downtime, developing reliability across power distribution networks, optimization of operational costs, and positioning it as a crucial tool for the future of smart grids.