Weather-Based Fault Detection in Solar Power Generation by ML

Solar energy, one of the most important renewable energies, employs many newer technologies, among which Solar Photovoltaic (PV) cells are extensively used for Generating Electricity. As for any installed system, the detection of faults in solar power systems is essential to ensure performance and reliability. It is determined that the solar irradiation, temperature, and shading of the PV cells affect the efficiency of the cells and, therefore, the diagnostics of the system and the cells’ malfunctions. Fault detection systems using statistical methods, such as Linear Regression and Decision Tree models, have been created and are now in use. Furthermore, newer machine learning algorithms such as AdaBoost, Random Forest, and Tweedie Regression are also employed to model the PV output under a range of weather conditions and evaluate how shading or module faults will affect it. These models enable precise fault detection because they distinguish between normal weather fluctuations and faults. The results suggest that machine learning, when combined with weather parameters, improves fault detection and also improves the system’s predictive maintenance capabilities. This creates better system performance and increases the lifetime of solar power systems.