Enhancing solar power generation efficiency through chaotic beetle swarm optimization for constant power generation - Sreenivasan Ramachandran; Ramkumar Ravindran (Bulletin of Electrical Engineering and Informatics, 2026)
The necessity for more and cleaner sustainable energy sources to generate power is increasing because of the reduction of fossil fuel supplies and their negative impacts on the environment. This research addresses the requiring crucial for optimized solar power systems in the weather change issues. In the beginning, the photovoltaic (PV) system’s voltage is enhanced by dual stage XBoost converter (DSXBC) with little voltage stress and maximum voltage gain. Also, the radial bias function neural network (RBFNN)maximum power point tracking (MPPT) tracks the PV system’s uppermost power and its parameters are fine-tuned by chaotic beetle swarm optimization (CBSO) algorithm. By integrating chaotic dynamics within the optimization process, CBSO runs a robust and efficient approach to navigating the complex search space related with MPPT. The MATLAB tool is utilized to reveal the efficacy of developed approach for allowing constant power generation in solar power generation systems with efficacy of 99.74% and tracking efficiency of 98.9% in steady state condition, thereby enabling continuous power generation.