Fault Localization in Photovoltaic Systems Using Machine Learning

This paper investigates the application of machine learning techniques for fault localization in photovoltaic (PV) systems. The research focuses on accurately identifying the location of faults within series-parallel connected PV panels. The same four machine learning algorithms (SVM, DT, FNN, RF) used in the previous study were employed for fault localization, using the same dataset. The performance of each algorithm was evaluated based on its ability to correctly pinpoint the faulty panel or module.