Hybrid Cascaded ANFIS-RNN-Based MPPT Controller For PV-Driven Grid System

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.