Original Article

Photovoltaic Power Generation Systems and Applications Using Particle Swarm optimization Algorithms


Marketing Service Center, State Grid Gansu Electric Power Company, Lanzhou, China


Department of Computer Science and Engineering, CMR College of Engineering & Technology, Hyderabad, India


Department of Chemical Engineering, College of Engineering, University of Bahrain, Kingdom of Bahrain


Department of CSE, University Centre for Research & Development, Chandigarh University, Mohali, India

ELECTRICA 2022; 22: 403-409
DOI: 10.5152/electrica.2022.22086
Read: 1481 Downloads: 422 Published: 26 September 2022

In order to alleviate the great harm caused by traditional fossil energy to the natural environment, photovoltaic power generation has become a good choice for human available resources. In this article, the photovoltaic array is used as the object of research, and the maximum power point tracking (MPPT) control algorithm of the photovoltaic array is used as the main research line. The photovoltaic array simulation model was simulated using the MATLAB/SIMULINK software to simulate the output characteristics. The following results were obtained. When the illumination intensity is fixed, the particle swarm optimization (POS) algorithm reaches the end condition after 0.015 s, stops the iteration, the photovoltaic array reaches the maximum power point, and the maximum power value found is 10.704 W, which is less than 1 W from the theoretical power value 10 W, with an error of 0.64%; this shows that the MPPT management approach based on the optimization algorithm has a positive effect. In the event of an immediate change in light, the particle optimization algorithm is re-optimized, with an optimization time of only 0.004 s, that is, it reaches the maximum power point and runs stably. This requires approximately 97% less time than the shock observation method and about 75% less time than the method required to increase the variable transmission, and the power curve is an almost smooth straight line. This shows that the PSO algorithm is able to quickly and accurately control the maximum power point in the event of a sudden change in lighting. The method optimized in this article is superior to the method of observing shocks in the case of stable lighting and sudden changes in light intensity also array and relatively high performance as well as high practical value.

Cite this article as: J. Wang, K. Wei, M. Dilshad Ansari, M. Saleh Al. Ansari and A. Verma, “Photovoltaic power generation systems and applications using particle swarm optimization algorithms,” Electrica, 22(3), 403-409, 2022.

EISSN 2619-9831