The clean energy aspect of solar Photovoltaic (PV) energy is becoming more popular in today's distribution networks, and the solar modules' output power is nonlinear owing to atmospheric conditions. To maximize the power generation from PV systems, efficient Maximum Power Point Tracking (MPPT) techniques and voltage regulation are crucial. Therefore, the proposed work incorporated the hybrid Cascaded Adaptive Network-Based Fuzzy Inference System (ANFIS) - Recurrent Neural Network (RNN) based MPPT and boost converter system for PV-tied grid systems. The proposed Boost converters are used to convert the PV panels' changing DC voltage into a stable and suitable voltage level for grid integration with high efficiency and low THD. Furthermore, to track optimal power from the PV system, cascaded ANFIS-RNN is employed. The cascaded ANFIS controller provides a robust and adaptive approach for tracking the Maximum Power Point (MPP), ensuring optimal extraction of power from PV panels, and to further enhance the MPPT performance, an RNN is integrated into the ANFIS controller, which leads to increased tracking precision and quicker convergence. The single-phase VSI is used to convert DC-AC supply for distributing power to the grid system, and it is controlled with the aid of PI controller. Finally, MATLAB/Simulink is used to implement the entire proposed concept, and a comparative analysis is made over with the existing topologies to prove the prominence of the developed work.
Rahiman et al. (Wed,) studied this question.