AI Based Predictive Maintenance for Smart Power Grids Using IoT and Edge Computing

Abstract

Smart strength grids can best function efficiently and cost effectively if equipped with reliable, sustainable protection systems that will be able to supply electricity continuously and minimize operating expenditure. Current maintenance practices are usually reactionary in nature, because fixes are only affected when faults occur, creating unpredictable disaster scenarios, increasing repair costs, and causing loss of plant capacity. Furthermore, lack of on-line measurement prevents early warning indication of degradation.

The paper presents an artificial intelligence based predictive maintenance system which harnesses the strengths of both IoT and edge computing for smart power grids. Sensors attached on grid components continuously record parameters such as voltage, current and temperature. Information gets handled on smaller devices instead of distant servers, speeding up work while reducing reliance on big centralized systems.

Old and new data get studied by computers, helping spot repeating issues tied to power failures. Because it forecasts trouble ahead, interventions happen earlier - availability stays strong, failures fade. Computing closer to where it operates cuts delays, opening up instant decisions instead of slow ones.

The proposed system offers a scalable and efficient answer for present day strength grid control, helping proactive renovation strategies and contributing to the improvement of smart and resilient electricity systems.