PHOTOVOLTAIC MPPT ADAPTIVE PARTICLE SWARM OPTIMIZATION OPTIMIZATION UNDER SHADING CONDITIONS

Han Sipeng; Jiang Xiaoyan; Luo Yi; Jiao Qianzhi; Tan Jingyang; Xun Yue

doi:10.19912/j.0254-0096.tynxb.2020-1001

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (6) : 99-105. DOI: 10.19912/j.0254-0096.tynxb.2020-1001

PHOTOVOLTAIC MPPT ADAPTIVE PARTICLE SWARM OPTIMIZATION OPTIMIZATION UNDER SHADING CONDITIONS

Han Sipeng¹, Jiang Xiaoyan², Luo Yi³, Jiao Qianzhi¹, Tan Jingyang¹, Xun Yue¹

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Abstract

In view of the multi-peak characteristic of the power-voltage characteristic curve of photovoltaic array in the case of local shadow, particle swarm optimization algorithm is applied to MPPT tracking in the case of local shadow, which has the disadvantages of slow search speed and low accuracy. The maximum power point tracking algorithm of adaptive inertia weight particle swarm optimization algorithm is proposed to automatically update the inertia weights w and learning factors C₁ and C₂. Through simulation experiments, the GMPP time before optimization is 0.045 s and the output power is 468 W. After optimization, the GMPP time of the adaptive particle swarm algorithm is 0.02 s and the output power is stabilized at 480 W. The output power tracking error of the PV array is less than 30%. In the simulation by the irradiance mutation model established in this paper, the tracking time is reduced by 55.5% and the tracking error is less than 5% compared with the traditional particle swarm algorithm. The feasibility and practicability of the algorithm are verified.

Key words

PSO / partial shading / MPPT / adaptive inertia weight / PV array

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Han Sipeng, Jiang Xiaoyan, Luo Yi, Jiao Qianzhi, Tan Jingyang, Xun Yue. PHOTOVOLTAIC MPPT ADAPTIVE PARTICLE SWARM OPTIMIZATION OPTIMIZATION UNDER SHADING CONDITIONS[J]. Acta Energiae Solaris Sinica. 2022, 43(6): 99-105 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1001

References

[1] 李山, 李科娜, 陈艳, 等. 基于改进粒子群和变步长扰动的复合MPPT算法[J].重庆理工大学学报(自然科学), 2019, 33(7): 207-213.
LI S, LI K N, CHEN Y, et al.Hybrid mppt algorithm based on improved particle swarm optimization and variable step disturbance[J]. Journal of Chongqing University of Technology(natural science), 2019, 33(7):207-213.
[2] YANG B, YU T, SHU H C, et al.Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers[J]. Applied energy, 2018, 210(C): 711-723.
[3] 张红民, 巴忠镭, 王艳, 等. 一种基于迁徙策略的自适应粒子群算法[J]. 重庆理工大学学报(自然科学), 2019, 33(11): 148-154.
ZHANG H M, BA Z L, WANG Y, et al.An adaptive particle swarm optimization algorithm based on migration strateg[J] . Journal of Chongqing University of Technology (natural science), 2019, 33(11): 148-154.
[4] DONG M, LYU D, YANG C, et al.Global maximum power point tracking of pv systems under partial shading condition: A transfer reinforcement learning approach[J]. Applied sciences, 2019, 9(13): 2769.
[5] NURIA NC, JOSE ANTONIO G P, VALLS-GUIRADO, et al. Optimal displacement of photovoltaic array’s rows using a novel shading mode[J]. Applied energy, 2015, 144(apr.15):1-9
[6] YUAN XL, YANG DF, LIU HM.MPPT of PV system under partial shading condition based on adaptive inertia weight particle swarm optimization algorithm[C]//2015 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent System(CYBER), Shenyang, China, 2015.
[7] KILLI M, SAMANTA S.Modified perturb and observe MPPT algorithm for drift avoidance in photovoltaic systems[J]. IEEE transactions on industrial electronics, 2015, 62(9): 5549-5559.
[8] JI Y H, JUNG D Y, KIM J G, et al.A real maximum power point tracking method for mismatching compensation in PV array under partially shaded conditions[J]. IEEE transactions on power electronics, 2011, 26(4): 1001-1009.
[9] PATEL H, AGARWAL V.Maximum power point tracking scheme for PV systems operating under partially shaded conditions[J]. Control engineering of China, 2014, 55(4): 1689-1698.
[10] SUNDARESWARAN K, VIGNESH KUMAR V, PALANI S.Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions[J]. Renewable energy, 2015, 75: 308-317.
[11] LI H, YANG D, SU W Z, et al.An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading[J]. IEEE transactions on industrial electronics, 2019, 66(1): 265-275.
[12] 王硕禾, 郑俊观, 陈祖成, 等. 基于改进粒子群优化算法光伏阵列多峰值MPPT的研究[J]. 可再生能源, 2019, 37(6): 879-885.
WANG S H, ZHENG J G, CHEN Z C, et al.Research on multi-peak MPPT of PV array based on improved Pso algorithm[J]. Renewable energy, 2019, 37(6): 879-885.
[13] 朱志国. 基于粒子群-细菌觅食算法的光伏发电MPPT[D]. 淮南: 安徽理工大学, 2019.
ZHU Z G.Photovoltaic power generation MPPT based on particle swarm optimization-bacterial foraging algorithm[D]. Huainan: Anhui University of Science and Technology, 2019.
[14] BENYOUCEF A S, CHOUDERA, KARA K, et al.Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions[J]. Applied soft computing, 2015, 32: 38-48.
[15] 訾鹏, 李轶群, 谭贝斯, 等. 大电网仿真工具现状及其在华北电网推广应用的思考[J]. 电力自动化设备, 2019, 39(9): 199-205.
ZI P, LI Y Q, TAN B S, et al.Current situation of large-scale power grid simulation tools and their popularization and application in North China Power Grid[J]. Electric power automation equipment, 2019, 39(9): 199-205.