针对传统最大功率点跟踪技术在局部遮阴等天气条件下存在无法追踪到全局最大功率点的问题,提出一种基于改进黏菌算法的MPPT控制。首先,对太阳电池模型及多峰值特性进行分析;其次,在黏菌算法中引入领导者策略和基于最优个体的凸透镜反向学习策略,在提高算法计算精度、收敛速度的同时克服了算法易“早熟”现象;最后,根据光伏阵列最大功率输出特性分别确定算法优化模型、初始化位置及重启机制。仿真与实验结果表明：基于改进黏菌算法的MPPT控制能快速、准确地跟踪到全局最大功率点,有效规避陷入局部最优问题,提高了光伏系统的转换效率。
Abstract
Aiming at the problem that the traditional maximum power point tracking technology can not track the global maximum power point under the special weather conditions such as partial shade, a MPPT control based on the improved slime mould algorithm is proposed. Firstly, the solar cell model and multi-peak characteristics are analyzed. Secondly, the leader strategy and the convex lens back learning strategy based on the optimal individual are introduced into the slime mould algorithm to improve the calculation accuracy and convergence speed of the algorithm and overcome the ‘premature’ phenomenon of the algorithm. Finally, according to the maximum power output characteristics of the photovoltaic array, the algorithm optimization model, initialization position and restart mechanism are determined respectively. The simulation and experimental results show that the MPPT control based on the improved slime mould algorithm can quickly and accurately track to the global maximum power point, effectively avoid falling into the partial optimal problem, and improve the output efficiency of the photovoltaic system.
关键词
光伏发电 /
最大功率点跟踪 /
优化 /
局部遮阴 /
改进黏菌算法
Key words
photovoltaic power generation /
maximum power point tracking /
optimization /
partial shading /
improved slime mould algorithm
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参考文献
[1] 杭凤海, 杨伟, 朱文艳. 光伏系统MPPT的扰动观测法分析与改进[J]. 电力系统保护与控制, 2014, 42(9): 110-114.
HANG F H, YANG W, ZHU W Y.Analysis and improvement of MPPT disturbance observer method for PV system[J]. Power system protection and control, 2014, 42(9): 110-114.
[2] TEY K S, MEKHILEF S.Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level[J]. Solar energy, 2014, 101(1): 333-342.
[3] 张明锐, 陈喆旸, 韦莉. 免疫萤火虫算法在光伏阵列最大功率点跟踪中的应用[J]. 电工技术学报, 2020, 35(7): 1553-1562.
ZHANG M R, CHEN Z Y, WEI L.Application of immune firefly algorithms in photovoltaic array maximum power point tracking[J]. Transactions of China Electrotechnical Society, 2020, 35(7): 1553-1562.
[4] 盛四清, 陈玉良, 张晶晶. 基于差分进化人工蜂群算法的光伏最大功率跟踪策略研究[J]. 电力系统保护与控制, 2018, 46(11): 23-29.
SHENG S Q, CHEN Y L, ZHANG J J.Research on maximum power point tracking strategy based on differential evolution artificial bee colony algorithm of photovoltaic system[J]. Power system protection and control, 2018, 46(11): 23-29.
[5] 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.
[6] 刘春娟, 郑丽君, 孙赟赟, 等. 基于改进型细菌觅食算法的MPPT[J]. 太阳能学报, 2021, 42(9): 83-89.
LIU C J, ZHENG L J, SUN Y Y, et al.Maximum power point tracking strategy based on improved bacterial foraging algorithm[J]. Acta energiae solaris sinica, 2021, 42(9): 83-89.
[7] 商立群, 朱伟伟. 基于全局学习自适应细菌觅食算法的光伏系统全局最大功率点跟踪方法[J]. 电工技术学报, 2019, 34(12): 2606-2614.
SHANG L Q, ZHU W W.Photovoltaic system global maximum power point tracking method based on the global learning adaptive bacteria foraging algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(12): 2606-2614.
[8] MOTAMARRI R, BHOOKYA N, CHITTI B B.Modified grey wolf optimization for global maximum power point tracking under partial shading conditions in photovoltaic system[J]. International journal of circuit theory and applications, 2021, 49(7) : 1884-1901.
[9] FIGUEIREDO S, NAYANA ALENCAR LEÃO E SILVA AQUINO R. Hybrid MPPT technique PSO-P&O applied to photovoltaic systems under uniform and partial shading conditions[J]. IEEE Latin America transactions, 2021, 19(10): 1610-1617.
[10] 朱梓嘉, 肖辉, 赵帅旗, 等. 基于并行组合进化算法的光伏阵列最大功率点追踪[J]. 电力系统保护与控制, 2020, 48(4): 1-10.
ZHU Z J, XIAO H, ZHAO S Q, et al.Maximum power point tracking of photovoltaic array based on parallel combination evolutionary algorithm[J]. Power System protection and control, 2020, 48(4): 1-10.
[11] 胡依林, 成奎, 杨博. 阴影条件下基于集体智慧的光伏系统最大功率跟踪[J]. 电力系统保护与控制, 2021, 49(24): 78-87.
HU Y L, CHENG K, YANG B.Collective intelligence-based maximum power point tracking of PV systems under partial shading condition[J]. Power system protection and control, 2021, 49(24): 78-87.
[12] LI S M, CHEN H L, WANG M J, et al.Slime mould algorithm: a new method for stochastic optimization[J]. Future generation computer systems, 2020, 111(1): 300-323.
[13] 秦文龙. 基于改进鲸鱼算法的光伏发电系统MPPT控制研究[D]. 曲阜: 曲阜师范大学, 2021.
QIN W L.Research on MPPT control of photovoltaic power generation system based on improved whale algorithm[D]. Qufu: Qufu Normal University, 2021.
[14] 袁清, 赵斌, 王力, 等. 基于TSA-P&O混合算法的光伏多峰值MPPT控制[J]. 电力系统及其自动化学报, 2021, 33(12): 101-109.
YUAN Q, ZHAO B, WANG L, et al.Multi-peak MPPT control of photovoltaic array based on TSA-P&O hybrid algorithm[J]. Proceedings of the CSU-EPSA, 2021, 33(12): 101-109.
[15] 赵帅旗, 肖辉, 刘忠兵, 等. 基于CSA-IP&O的局部遮阴下光伏最大功率点追踪[J]. 电力系统保护与控制, 2020, 48(5): 26-32.
ZHAO S Q, XIAO H, LIU Z B, et al.Photovoltaic maximum power point tracking under partial shading based on CSA-IP&O[J]. Power system protection and control, 2020, 48(5): 26-32.
[16] 杨博, 钟林恩, 朱德娜, 等. 部分遮蔽下改进樽海鞘群算法的光伏系统最大功率跟踪[J]. 控制理论与应用, 2019, 36(3): 339-352.
YANG B, ZHONG L E, ZHU D N, et al.Modified salp swarm algorithm based maximum power point tracking of power-voltage system under partial shading condition[J]. Control theory & applications, 2019, 36(3): 339-352.
基金
辽宁省教育厅科研项目（LJKZ0332）; 辽宁省高等学校国（境）外培养项目（2019GJWZD002）
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