This paper offers a tracking algorithm for maximum power point tracking in both dynamic and steady-state conditions of a partially shaded solar photovoltaic (PV) system. The algorithm is based on the Sovereign Butterfly Optimization and a flyback converter. In just a few simple steps, this method may be used to monitor the global optimum peak location quickly and without oscillations. This method for maximum power point tracking (MPPT) in partially shaded conditions has a distinct advantage over other evolutionary techniques because it does not suffer from the typical and widespread issues that cause power loss and output oscillations, such as a lengthy convergence duration, an excessive number of search particles,steady state oscillation, and heavy computational burden.In MATLAB, we simulate a voltage-power curve with many peaks to see how well this technique works. In addition, we compare the tracking capabilities to state-of-the-art approaches. In comparison to state-of-the-art control approaches, the new technology demonstrates improved dynamic and steady-state performances under varying irradiance and temperature levels.The proposed method integrates Sovereign Butterfly Optimization (SBO) with a flyback converter to overcome key limitations such as lengthy convergence, steady-state oscillations, and high computational burden. Compared to existing algorithms such as PSO, the proposed technique achieves up to 24 times faster convergence, over 99.7% MPPT efficiency, and less than 1% deviation in HIL validation. Simulation results and hardware-in-the-loop testing using OPAL-RT (OP4510) confirm the robustness and high tracking precision of the SBO-based approach. Additionally, real-time hardware-in-the-loop (HIL) testing using the OPAL-RT (OP4510) platform validates the practical performance of the algorithm under dynamic and steady-state conditions, effectively bridging the gap between simulation and real-world implementation.
Kanth et al. (2025) studied this question.