Hybrid Cascaded ANFIS–RBFNN-Based Controller For PV-Driven Grid System

Solar photovoltaic (PV) energy is gaining popularity in modern distribution networks due to its clean energy attributes. In order to maximize PV power generation conversion, the application of maximum power point tracking (MPPT) is essential. Henceforth, this work presents a novel hybrid MPPT approaches based on a cascaded adaptive neuro fuzzy inference system and radial basis function neural network to achieve rapid and greatest PV power extraction while ensuring zero oscillation tracking with a single-ended primary inductor converter (SEPIC). SEPIC efficiently regulates the output voltage to match grid requirements while maintaining high power conversion efficiency. The cascaded artificial neuro fuzzy inference system (ANFIS) and radial basis function neural network (RBFNN) are combined to enhance MPPT accuracy and robustness under varying environmental conditions. The cascaded architecture enables a seamless transition between the two controllers, ensuring optimal performance across an extensive range of operating conditions. The MATLAB/Simulink is used for analyzing the efficacy of the proposed system and the proposed converter and MPPT approach is compared with existing topologies for proving the importance of the developed work. The outcomes demonstrate that the proposed SEPIC achieves reduced Total Harmonic Distortion (THD) of 1.16% and the cascaded ANFIS–RBFNN-based MPPT approach attains a higher tracking efficiency of 99.61% with rapid convergence speed and execution time compared to traditional techniques. Overall, this paper represents a promising direction toward achieving more efficient and sustainable PV-driven grid integration.