Grid-Connected Single-Stage DC-AC Converter for Solar PV Applications

Photovoltaic (PV) has become an important alternative because of the increased demand for electricity and the limited supply of traditional energy sources. Harmonic distortion (HD) on the grid-connected PV can lead to several undesirable outcomes, including overheating of connected equipment, frequent grid interruptions, reduced accuracy of electrical meters, and an increase in the required current. This manuscript proposes the novel use of the Sunflower Optimization (SFO) Algorithm in grid-connected single-stage DC-AC converter with minimizing Total HD (THD) and enhancing the efficiency of the system as its primary objective. The SFO algorithm is used to maximize the system's features and control the switched inductor boost converter. The proposed technique is then simulated using MATLAB and its performance is contrasted with other existing technologies Recurrent Neural Network (RNN), Garra-Rufa Fish Optimization-Student Psychology Optimization Algorithm (GRFO-SPOA), and Fuzzy Logic Control (FLC). The proposed method attains an efficiency of 97% with a low THD of 3.1%. The findings suggest that the proposed method outperforms the existing approaches.