Optimizing Photovoltaic System Efficiency: A Comparative Study of RNN-based Controller and Incremental Conductance in MPPT

In this study, a Recurrent Neural Network (RNN)- based PV controller is thoroughly examined and compared with conventional Incremental Conductance techniques for Maximum Power Point Tracking (MPPT). The study emphasizes the improved responsiveness and efficiency of the RNN controller and focuses on the dynamic adaptation of PV systems to changing environmental conditions. In this study, the better performance of the RNN controller in monitoring the maximum power point, its fast convergence time, and its stability under various environmental conditions are demonstrated by comprehensive simulations and comparative studies. According to the results, using cutting-edge machine learning methods, such as RNNs, may greatly increase PV systems' operational effectiveness, highlighting their potential to optimize renewable energy systems. This study offers insights that advance the realm of renewable energy technologies.