Solar Panel Fault Analysis Using Regression Models

Solar photovoltaic (PV) systems are critical in harnessing renewable energy from the sun, yet they are susceptible to faults that can significantly impair their efficiency and lifespan. This work addresses the urgent need for advanced fault detection methodologies, utilizing regression models to ensure the optimal performance and reliability of solar energy installations. This paper presents a comprehensive analysis of solar panel fault detection using advanced regression models, marking a significant contribution to the domain of renewable energy efficiency enhancement. The study investigates the relationship between various parameters—such as ambient temperature, irradiance, DC and AC power output—and the performance efficiency of solar photovoltaic (PV) systems. Employing a data-driven approach, the research integrates linear and non-linear regression models, alongside logistic regression for binary classification of panel states, to predict and diagnose faults within solar panels effectively. Through the analysis of a dataset obtained from two solar power facilities in India, the work demonstrates the capability of regression models to identify discrepancies between expected and actual power outputs, thereby pinpointing potential faults. This novel methodology offers a more accurate, reliable, and efficient means of maintaining solar PV systems at peak performance, ensuring sustainable energy production. The findings underscore the critical role of advanced data analysis techniques in the early detection of faults, which is paramount to reducing maintenance costs, enhancing energy yield, and extending the lifespan of solar installations.