Suffering from the multiple peaks of PV modules under local shadows,the traditional MPPT algorithms cannot accurately track their maximum power point. In the paper, three MPPT algorithms of PV modules based on artificial intelligence algorithms are studied, including particle swarm optimization algorithm, gray wolf algorithm, and improved artificial bee colony algorithm. This paper provides a detailed introduction to the principle and process of three artificial intelligence algorithms,and a simulation model of the system is established in Matlab/Simulink. By comparing the MPPT tracking performance of the three algorithms under static shadow occlusion and sudden shadow changes, the simulation results show that all three artificial intelligence algorithms can effectively track the maximum power point of PV modules, with the tracking errors less than 0.5%. Among them, particle swarm optimization algorithm has the highest tracking accuracy and the slowest convergence speed. The grey wolf algorithm has the lowest tracking accuracy and the fastest convergence speed. In terms of convergence stability, compared to the grey wolf algorithm and the improved artificial bee colony algorithm,the particle swarm optimization algorithm is more prone to track the local optima.
Key words
PV modules /
maximum power point tracing /
particle swarm optimization /
grey wolf algorithm /
improved artificial bee colony algorithm
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References
[1] 国家能源局. 国家能源局发布2021年全国电力工业统计数据[EB/OL].http://www.nea.gov.cn/2022-01/26/c_1310441589.htm.
National Energy Administration. National Energy Administration Releases2021 National Power Industry Statistical Data[EB/OL]. http://www.nea.gov.cn/2022-01/26/c_1310441589.htm.
[2] 苏建徽, 余世杰, 赵为, 等. 硅太阳电池工程用数学模型[J]. 太阳能学报, 2001, 22(4): 409-412.
SU J H, YU S J, ZHAO W, et al.Investigation on engineering analytical model of silicon solar cells[J]. Acta energiae solaris sinica,2001, 22(4): 409-412.
[3] 周元贵, 陈启卷, 何昌炎, 等. 局部阴影下光伏阵列建模及多峰值MPPT控制[J]. 太阳能学报, 2016, 37(10): 2484-2490.
ZHOU Y G, CHEN Q J, HE C Y, et al.Model of pv array under partial shading and mppt control of multi-peak characteristics[J]. Acta energiae solaris sinica, 2016, 37(10): 2484-2490.
[4] 徐伟. 光伏发电系统的建模及智能MPPT算法研究[D]. 上海: 东华大学, 2021.
XU W.Modeling of photovoltaic power generation system and research on intelligent MPPT algorithm[D]. Shanghai: Donghua University, 2021.
[5] 钟黎萍, 张水平, 顾启民. 基于基因排序遗传算法的串联光伏组件MPPT研究[J]. 可再生能源, 2017, 35(3): 384-388.
ZHONG L P, ZHANG S P, GU Q M.Study on the MPPT of series photovoltaic modules based on gene sequencing GA[J]. Renewable energy resources, 2017, 35(3): 384-388.
[6] CHIN C S,TAN M K,NEELAKANTAN P, et al.Optimization of partially shaded PV array using fuzzy MPPT[C]//2011 IEEE Colloquium on Humanities,Science and Engineering. Penang, Malaysia, 2012: 481-486.
[7] 韩思鹏, 蒋晓艳, 罗意, 等. 遮阴条件下光伏MPPT自适应粒子群算法优化[J]. 太阳能学报, 2022, 43(6): 99-105.
HAN S P, JIANG X Y, LUO Y, et al.Photovoltaic mppt adaptive particle swarm optimization optimization under shading conditions[J]. Acta energiae solaris sinica, 2022,43(6): 99-105.
[8] 赵斌, 袁清, 王力, 等. 基于改进蚁狮算法的光伏多峰值MPPT控制[J]. 太阳能学报, 2021, 42(9): 132-139.
ZHAO B, YUAN Q, WANG L, et al.Multi-peak MPPT control of PV array based on improved alo algorithm[J]. Acta energiae solaris sinica, 2021, 42(9): 132-139.
[9] 李泽. 局部阴影下光伏电池最大功率点跟踪方法研究[D]. 长春: 吉林大学, 2021.
LI Z.Research on tracking method of maximum power point of photovoltaic cells in partial shadows[D]. Changchun: Jilin University, 2021.
[10] 沈磊, 徐岸非, 黄晴宇, 等. 基于GWO-P&O算法的局部阴影光伏MPPT研究[J]. 湖北工业大学学报, 2022, 37(2): 25-29, 43.
SHEN L,XU A F,HUANG Q Y,et al.Research on MPPT of photovoltaic under partial shading condition based on GWO-P&O algorithm[J]. Journal of Hubei University of Technology, 2022, 37(2): 25-29, 43.
[11] 聂莉. 局部阴影下光伏发电系统MPPT控制策略研究[D]. 重庆: 重庆大学, 2019.
NIE L.Research on MPPT control strategy of photovoltaic power generation system under partial shadow[D]. Chongqing: Chongqing University, 2019.
[12] SHI Y, EBERHART R.Empirical study of particle swarm optimization[C]// International Conference on Evolutionary Computation. Washington, USA, 1999.
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