Abstract:

The traditional maximum power point tracking (MPPT) algorithm is prone to fall into local optimization in the case of a multipeak photovoltaic array. The butterfly optimization algorithm has a strong global search capability and a relatively stable convergence process; however, it has not been widely used due to its low convergence accuracy. This paper proposes an MPPT algorithm that combines the improved butterfly optimization algorithm with the perturbation and observation method. The traditional butterfly optimization algorithm was optimized by introducing the chaotic mapping theory to improve the distribution of the initial butterfly population. Besides, the dynamic switching probability was used to optimize the switching strategy. Herein, first, the global search capability of the butterfly optimization algorithm was used to locate the range of the maximum power point, and then the small step size perturbation and disturbance observation method were used to accurately locate the maximum power point. This algorithm combines the advantages of the global optimization of the butterfly optimization algorithm and the precise optimization of the perturbation and observation method. Furthermore, Simulink simulation experiments were conducted, and the results were compared with the traditional butterfly optimization algorithm and particle swarm optimization algorithm. The results show that the improved algorithm can adapt to complex and changing light conditions and has certain advantages in both convergence accuracy and speed.

Key words: photovoltaic power generation, maximum power point tracking, butterfly optimization algorithm, perturbation and observation method, chaotic mapping