Performance Analysis of ML Algorithm for Solar PV Fault Diagnosis

This research study has proposed an advanced solar fault detection system, which incorporates real-time monitoring and machine learning for improving the reliability of the solar power system. The system collects data using voltage, current, temperature, and irradiance sensors for fault detection, such as module degradation, overheating, and shading. Historical data modeled through machine learning algorithms such as SVM, Decision Trees, and Random Forest is then analyzed to predict or diagnose faults before it reaches the next level. This system is expected to improve performance, reduce costs of maintenance and ensure effective energy production. It will be scalable, affordable, and integrable into the larger smart grid platforms aimed at making solar energy even more efficient.