Based on the refined hierarchical population algorithm, it is applied to the research of photovoltaic array MPPT

This study investigates the challenge of slow convergence rate in the sparrow search algorithm (SSA) for maximum power point tracking (MPPT). A novel MPPT control methodology is proposed, which incorporates an enhanced hierarchical population algorithm. The methodology involves three key innovations: firstly, a particle population suspension mechanism is implemented; secondly, a collaborative optimization framework between sparrow and particle populations is established, employing a refined hierarchical sequence to generate uniformly distributed initial populations; finally, boundary constraint functions and symbolic matrices are integrated to enhance tracking precision and efficiency. These improvements significantly reduce power output oscillations and effectively mitigate tracking errors and power losses. Simulation results demonstrate that the proposed approach ensures both stability and rapid response in maximum power point tracking, even under dynamic environmental conditions, thereby enhancing the overall performance of photovoltaic power generation systems.