AI Based Predictive Maintenance and Fault Detection of Solar Based Microgrids

The utilization of solar-powered microgrids is on the rise as a way to assist in integrating renewable energy sources; however - due to unanticipated component failures and extensive dependence on manual maintenance - these systems often lose operational reliability. This paper proposes a new predictive maintenance and fault detection system based on artificial intelligence (AI) for solar-electric microgrids that will subsequently increase their reliability, efficiency, and cost-effectiveness. The proposed framework utilizes telemetry data gathered from sensors measuring voltage, current, temperature, and vibration and relayed through IoT-enabled data communication modules to a central processing unit. By integrating the fault detection capability with the predictive maintenance capability, this will allow for the implementation of timely maintenance activities by way of intelligent prompts and support for decision-making. It is anticipated that the newly developed predictive maintenance and fault detection system will achieve very high rates of fault detection accuracy (80%–90%), decrease the amount of time the system is not functioning, and improve the overall performance of renewable energy-based microgrids.