Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems

In this study, an improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems is investigated. The effect of PV partial shading conditions, uniform and fast-tracking irradiance, duty cycle, frequency, temperature changes, and load types, and besides some comparative studies of different algorithms are adequately examined for better performance study of the proposed technique. The proposed improved salp swarm algorithm based particle swarm optimization utilizes the PV Solarex-MSX-60 photovoltaic solar panel, which considers voltage and current as inputs based on the proposed algorithm parameters selection. Besides, it uses a buck-boost converter as an interface between input and output. The particle swarm optimization monitors the PV voltage and current, and the salp swarm algorithm does for the duty cycle (particles) in various environmental conditions. The proposed algorithm performs efficiencies 99.99%, 99.63%, and 99.24% comparison with other methods, under uniform irradiance and fast-tracking irradiance respectively. Moreover, the highest power of 316.32 W reached at the duty cycle of 0.6 and 428.6 W at the frequency of 30 kHz under the same partial shading condition with optimal operating temperature values 10 degrees C,15 degrees C,20 degrees C,25 degrees C,30 degrees C,35 degrees C.

Automated summary

In this study, an improved salp swarm algorithm based on particle swarm optimization is proposed for maximum power point tracking of optimal photovoltaic systems. The algorithm performs well under different shading conditions, irradiances, duty cycles, frequencies, temperatures, and load types. Compared to other methods, the proposed algorithm achieves high efficiencies and power outputs.