Authors - G Venkata Suresh Reddy, Immanuel Anupalli, P.Sudheer Abstract - Solar photovoltaic (PV) systems require robust and intelligent problem detection systems to guarantee they continue producing energy effectively as they gain traction as a renewable energy source. In order to detect various defects in photovoltaic (PV) systems operating under nonlinear and noisy conditions, this research presents a data-driven fault classification framework that employs machine learning techniques. Electrical data from photovoltaic (PV) panels, including current-voltage (I-V) and power-voltage (P-V) curves recorded in three distinct operating circumstances (Healthy, Shading, and Open-Circuit), formed the basis of the dataset used for training and testing the model. For each condition, crucial electrical characteristics have been used to characterize the system's electrical behavior, including open-circuit voltage, short-circuit current, maximum power point voltage and current, fill factor, and a handful of statistical statistics. Logistic Regression, Naïve Bayes, and k-Nearest Neighbors (KNN) are the three supervised machine learning methods that were employed to detect various errors. Each model was fine-tuned using hyper parameter tweaking and k-fold cross-validation. The classification performance in the comparative performance analysis was greatest for Logistic Regression (96.09% accuracy, 96.25% precision, 96.49% recall, and 96.36% F1-score). Second place went to the KNN model, which had a 95.47% accuracy rate. In contrast, the Naïve Bayes model maintained its reliability, with an accuracy rate of 94.13%. This demonstrates that it is still effective when dealing with nonlinear data that contains noise. According to the overall results, many machine learning algorithms, especially Logistic Regression, do a great job of finding PV problems in real time. The suggested framework is both efficient and useful for real-world PV monitoring systems because it just needs to measure electrical parameters that are easy to get (I-V and P-V data). Using this strategy for preventative maintenance makes solar systems more reliable and increases their production, which in turn cuts down on power losses.
Open Zoom