基于改进差分进化算法的光伏最大功率点跟踪

doi:10.16180/j.cnki.issn1007-7820.2022.09.003

Abstract

Abstract:

For the photovoltaic maximum power point tracking algorithm, the convergence of traditional algorithms will cause the problem of trapping into local extremes, while the convergence of intelligent algorithms is slow, and may produce large amplitude voltage oscillation in the process of convergence. In view of the above-mentioned problems, the study proposes corresponding improvement strategies. In the iterative process of particle swarm optimization, the individual order of the population is added to suppress the large amplitude voltage oscillation, the influence of the particles with poor fitness on the speed update is eliminated, and the convergence speed is improved through combining the competitive relationship during population update in the differential evolution algorithm. Single-peak and multi-peak examples are used to simulate the proposed strategy, and the maximum photovoltaic power obtained is 60 W and 122 W, respectively. The simulation results show that compared with particle swarm optimization and differential evolution algorithm, the proposed algorithm improves the convergence speed by 52.22% and 61.60% respectively, and reduces the mid-term voltage fluctuation amplitude by 15% and 30%, respectively.

Key words: solar cell, partial shading, characteristic curve, MPPT, particle swarm, differential evolution, individual ranking, particle fitness, global maximum power point

CLC Number:

TP13

GE Chuanjiu,WU Peng,JIN Junzhe,DONG Xiangxiang,LOU Qikai. Photovoltaic Maximum Power Point Tracking Based on Improved Differential Evolution Algorithm[J].Electronic Science and Technology, 2022, 35(9): 15-21.

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工况	光伏组件的光照强度/W·m^-2
工况	组件1	组件2	组件3
工况1	1 000	1 000	1 000
工况2	1 000	800	1 000
工况3	600	800	1 000

	PSO	DE	IPSO-DE
前期电压/V	6~38	10~38	13~35
中前电压/V	13~36	3~38	13~33
中后期电压/V	15~34	7~38	14~33
后期电压/V	16~30	3~38	18~30

Photovoltaic Maximum Power Point Tracking Based on Improved Differential Evolution Algorithm

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