Enhanced Fault Classification in Photovoltaic Panels Using Random Forest and k-Nearest Neighbors

The chapter delves into the critical importance of fault classification in photovoltaic (PV) systems, which are increasingly vital for sustainable energy solutions. It begins by outlining the various defects that can occur in PV systems, such as aging, shading, hot spots, and circuit issues, and the methods used to detect these faults. The document explores two primary techniques for fault detection: visual and thermal techniques (VTMs) and electricity-based methods (EBMs), with a focus on the current-voltage (I-V) characteristics analysis (IVCA) due to its simplicity and effectiveness. The core of the chapter compares the performance of two machine learning algorithms, k-Nearest Neighbors (k-NN) and Random Forest (RF), in classifying faults in PV systems. Through extensive simulations and data analysis, the study demonstrates that the Random Forest algorithm consistently outperforms k-Nearest Neighbors in terms of accuracy, reliability, and generalization across different data set sizes. The chapter also discusses the mathematical modeling of solar cells and the impact of various faults on the I-V and power-voltage (P-V) characteristics of PV modules. It provides a comprehensive analysis of how different faults manifest in the characteristic curves, which is crucial for accurate fault diagnosis and system optimization. The results highlight the superior classification capability of the RF algorithm, making it a preferred choice for enhancing the reliability and efficiency of PV systems. The chapter concludes with a discussion on the practical implications of these findings, emphasizing the importance of advanced fault classification methods in ensuring the longevity and performance of PV systems.