Comparation analysis of SSA and GWO algorithms for maximum power point tracking in standalone solar modules

Abstract

Solar photovoltaic (PV) systems experience continuous output fluctuations due to changes in solar irradiance and operating temperature, reducing the effectiveness of power extraction. To improve energy harvesting capability, an adaptive maximum power point tracking (MPPT) method is required. This study evaluates the performance of the salp swarm algorithm (SSA) and grey wolf optimization (GWO) for MPPT control in a 100 Wp standalone PV system employing a single-ended primary inductor converter (SEPIC). The analysis focuses on tracking speed, efficiency, and stability under varying environmental conditions. Simulations were carried out in the ALTAIR PSIM Professional 2022.1.0.8 platform with irradiance levels ranging from 200-1000 W/m² and temperatures between 35-55 °C, including dynamic irradiance transitions. The obtained results show that SSA achieved a higher average tracking efficiency of 97.46% with a convergence time of 0.2276 s, while GWO produced 87.94% efficiency and a 0.2512 s convergence time. In addition, SSA demonstrated more stable tracking behavior and lower oscillation during low-irradiance operation. These results indicate that SSA provides better MPPT performance for compact standalone PV applications operating under fluctuating environmental conditions. Future work will involve hardware-based validation and real-time implementation.