针对复杂遮阴环境下光伏阵列P-U曲线呈现多个峰值而导致传统最大功率点跟踪（MPPT）算法易陷入局部最优解的问题,提出一种新的控制算法模型——LGPSO_P&O模型。该模型结合Levy飞行、高斯变异策略和粒子群算法,并对粒子群算法惯性权重进行改进,构建LGPSO算法搜索全局最大功率点（MPP）。同时,改进扰动观察法（P&O）对MPP进行局部跟踪,进一步提高跟踪精度。在Matlab/Simulink中进行仿真实验,并与改进PSO、BPSHO和GA_ACO算法作对比,仿真结果表明该模型具有较高的鲁棒性,收敛速度快,跟踪精度高,最高可达99.8%。
Abstract
Aiming at the problem that the P-U curve of photovoltaic array presents multiple peaks in complex shading environment, which causes the traditional maximum power point tracking （MPPT） algorithm to fall into the local optimal solution, a new control algorithm model, LGPSO_P&O model, is proposed. The model combines Levy flight, Gaussian variation strategy and particle swarm algorithm, and improves the inertial weights of particle swarm algorithm to construct the LGPSO algorithm to search for global maximum power points （MPP）. At the same time, the disturbance observation method （P&O） is improved to track MPP locally to further improve the tracking accuracy. Simulation experiments are carried out in MATLAB/Simulink, and compared with improved PSO, BPSHO and GA_ACO algorithms, the simulation results show that the model has high robustness, fast convergence speed, high tracking accuracy, up to 99.8%.
关键词
光伏阵列 /
局部遮阴 /
最大功率点跟踪 /
粒子群算法 /
Levy飞行 /
变步长扰动观察法
Key words
PV arrays /
partial shaded /
MPPT /
particle swarm optimization /
Levy flight /
variable step size perturbation observation method
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参考文献
[1] ESRAM T, CHAPMAN P L.Comparison of photovoltaic array maximum power point tracking techniques[J]. IEEE transactions on energy conversion, 2007, 22(2): 439-449.
[2] SUO C G, ZHANG W B, WU G M, et al.Modelling and simulation of MPPT algorithm for PV grid-connected system[J]. Integrated ferroelectrics, 2015, 162(1): 18-23.
[3] 姜萍, 栾艳军, 张伟, 等. 局部遮阴下基于改进PSO的多峰值MPPT研究[J]. 太阳能学报, 2021, 42(8): 140-145.
JIANG P, LUAN Y J, ZHANG W, et al.Research of mult-peak MPPT under partial shaded conditions based on improved PSO algorithm[J]. Acta energiae solaris sinica, 2021, 42(8): 140-145.
[4] 黄荣赓, 陈路遥. 基于蝙蝠与粒子群混合优化算法的光伏MPPT研究[J]. 电源技术, 2022, 46(3): 324-328.
HUANG R G, CHEN L Y.Photovoltaic MPPT study based on bat and particle swarm hybrid optimization algorithm[J]. Chinese journal of power sources, 2022, 46(3): 324-328.
[5] CHAO K H, RIZAL M N.A hybrid MPPT controller based on the genetic algorithm and ant colony optimization for photovoltaic systems under partially shaded conditions[J]. Energies, 2021, 14(10): 2902.
[6] 唐圣学, 张启然, 刘亚敬, 等. 太阳电池动态模型仿真分析及实验研究[J]. 太阳能学报, 2019, 40(9): 2536-2546.
TANG S X, ZHANG Q R, LIU Y J, et al.Simulation and experimental study on dynamic model of photovoltaic cells[J]. Acta energiae solaris sinica, 2019, 40(9): 2536-2546.
[7] KENNEDY J, EBERHART R.Particle swarm optimization[C]//Proceedings of ICNN’95-International Conference on Neural Networks. Perth, WA, Australia, 1995: 1942-1948.
[8] 赵其浩, 李田泽, 邵泰衡, 等. 莱维飞行优化果蝇算法在光伏MPPT中的研究[J]. 现代电子技术, 2019, 42(20): 76-80.
ZHAO Q H, LI T Z, SHAO T H, et al.Research on Levy flight optimization Drosophila algorithm in photovoltaic MPPT[J]. Modern electronics technique, 2019, 42(20): 76-80.
[9] 花赟昊, 朱武, 靳一奇, 等. 基于自适应变异粒子群算法的光伏MPPT控制研究[J]. 太阳能学报, 2022, 43(4): 219-225.
HUA Y H, ZHU W, JIN Y Q, et al.Research on photovoltaic MPPT control based on adaptive mutation particle swarm optimization algorithm[J]. Acta energiae solaris sinica, 2022, 43(4): 219-225.
[10] 刘倩, 冯艳红, 陈嶷瑛. 基于混沌初始化和高斯变异的飞蛾火焰优化算法[J]. 郑州大学学报(工学版), 2021, 42(3): 53-58.
LIU Q, FENG Y H, CHEN Y Y.Moth-flame optimization algorithm based on chaotic initialization and Gaussian mutation[J]. Journal of Zhengzhou University (engineering science), 2021, 42(3): 53-58.
[11] 程世辉, 卢翠英. 算法的时间复杂度分析[J]. 河南教育学院学报(自然科学版), 2007, 16(4): 20-23.
CHENG S H, LU C Y.Analysis of algorithmic time complexity[J]. Journal of Henan Institute of Education (natural science edition), 2007, 16(4): 20-23.
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