摘要
Maximum power point tracking(MPPT) techniques are used to maintain photovoltaic modules operating points at the local maximum power points under non-uniform irradiance conditions(NUIC). For global maximum power point tracking(GMPPT) within an appropriate period, a hybrid artificial fish swarm algorithm(HAFSA) is proposed in this paper, which was developed using particle swarm optimization(PSO) to reformulate AFSA and improve its principal parameters. Simulation results show that under NUIC, compared with PSO and AFSA, the proposed algorithm has better performance with respect to convergence speed and convergence accuracy. Under NUIC, the average convergence times for 1000 simulation experiments completed with PSO, AFSA, and HAFSA are 0.4830 s, 0.4003 s and 0.3152 s respectively, and the average tracking time of the HAFSA algorithm is reduced by 34.74% and 21.26% compared with PSO and AFSA, respectively. The convergence times of the velocity inertia ω relative constant and linear decrement method decreased by 35.48% and 8.19%, the convergence time of the Visual relative constant mode decreased by 10.16%, and the convergence time of the Step relative constant mode decreased by 17.88%. The proposed GMPPT algorithm is simulated in MATLAB, and the algorithm tracks GMPP with excellent efficiency and fast speed.
Maximum power point tracking(MPPT) techniques are used to maintain photovoltaic modules operating points at the local maximum power points under non-uniform irradiance conditions(NUIC). For global maximum power point tracking(GMPPT) within an appropriate period, a hybrid artificial fish swarm algorithm(HAFSA) is proposed in this paper, which was developed using particle swarm optimization(PSO) to reformulate AFSA and improve its principal parameters. Simulation results show that under NUIC, compared with PSO and AFSA, the proposed algorithm has better performance with respect to convergence speed and convergence accuracy. Under NUIC, the average convergence times for 1000 simulation experiments completed with PSO, AFSA, and HAFSA are 0.4830 s, 0.4003 s and 0.3152 s respectively, and the average tracking time of the HAFSA algorithm is reduced by 34.74% and 21.26% compared with PSO and AFSA, respectively. The convergence times of the velocity inertia ω relative constant and linear decrement method decreased by 35.48% and 8.19%, the convergence time of the Visual relative constant mode decreased by 10.16%, and the convergence time of the Step relative constant mode decreased by 17.88%. The proposed GMPPT algorithm is simulated in MATLAB, and the algorithm tracks GMPP with excellent efficiency and fast speed.
基金
supported by National Natural Science Foundation of China (No.61501106)
Science and Technology Foundation of Jilin Province (No. 20180101039JC and JJKH20170102KJ)
