光伏温差混合发电（PV-TEG）系统在局部阴影情形下功率-电压（P-U）特性曲线呈现多峰值,传统最大功率点追踪（MPPT）方法易陷入局部最优值,导致系统发电功率降低,智能算法虽能一一追踪到全局最大功率点,但追踪时间过长导致能量损失。针对上述问题,提出一种基于改进雪消融优化器（ISAO）的PV-TEG最大功率点追踪（MPPT）方法,首先设计基于PV-TEG特性的初始位置策略,使得初始位置获得更好的适应值;其次在原始SAO的位置更新策略基础上引入基于种群边界范围的动态参数调整机制,并设计改进的开发搜索方式,实现全局与局部搜索能力动态平衡。最后,通过仿真与实验分析,与粒子群算法（PSO）比较,证明了所提方法跟踪能力更佳。
Abstract
The photovoltaic thermoelectric generation system exhibits multiple peaks in the power voltage characteristic curve under partial shadow conditions, and traditional methods are prone to fall into local values, resulting in a decrease in the system’s power generation. Although the intelligent algorithms can track the global maximum value, the tracking time is too long, resulting in energy loss. A maximum power point tracking （MPPT） method for photovoltaic thermoelectric generation system （PV-TEG） based on improved snow ablation optimizer （ISAO） was proposed to address the above issues. Firstly, an initial position strategy based on the characteristics of PV-TEG was designed to achieve better adaptation values for the initial position; Secondly, based on the original position update strategy of SAO, a dynamic parameter adjustment mechanism based on population boundary range is introduced, and an improved exploit search method is designed to achieve dynamic balance between global and local search capabilities. Finally, through simulation and experimental analysis, it is demonstrated that the proposed method has better tracking ability compared to particle swarm optimization （PSO）.
关键词
光伏温差混合发电 /
最大功率点追踪 /
局部阴影 /
改进雪消融优化器
Key words
photovoltaic thermoelectric generation /
maximum power point tracking /
local shadow /
improved snow ablation optimizer
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参考文献
[1] 杨博, 胡袁炜骥, 郭正勋, 等. 基于人工蜂群算法的温差发电阵列最优重构方法[J]. 上海交通大学学报, 2024, 58(1): 111-126.
YANG B, HU Y W J, GUO Z X, et al. Optimal reconfiguration method for thermoelectric power array based on artificial bee colony algorithm[J]. Journal of Shanghai Jiao Tong University, 2024, 58(1): 111-126.
[2] 杨博, 王加荣, 黄剑湘, 等. 基于海马优化器的光伏-温差混合系统重构[J]. 电网技术, 2023, 47(4): 1386-1403.
YANG B, WANG J R, HUANG J X, et al.Reconfiguration of PV-TEG hybrid system based on sea horse optimizer[J]. Power system technology, 2023, 47(4): 1386-1403.
[3] 石季英, 凌乐陶, 薛飞, 等. 领地粒子群算法在光伏最大功率跟踪的应用[J]. 太阳能学报, 2019, 40(9): 2554-2560.
SHI J Y, LING L T, XUE F, et al.Application of territorial PSO in global MPPT for PV array[J]. Acta energiae solaris sinica, 2019, 40(9): 2554-2560.
[4] 王小飞. 聚光光伏温差联合发电系统MPPT控制研究与仿真分析[D]. 哈尔滨: 东北农业大学, 2022.
WANG X F.Research and simulation analysis on MPPT control of concentrated photovoltaic combined power generation system with temperature difference[D]. Harbin: Northeast Agricultural University, 2022.
[5] 张梦婷. 集中式温差发电系统最大功率点跟踪智能控制策略研究[D]. 昆明: 昆明理工大学, 2022.
ZHANG M T.Research on intelligent control strategy of maximum power point tracking in centralized thermoelectric generation system[D]. Kunming: Kunming University of Science and Technology, 2022.
[6] 王立舒, 白龙, 房俊龙, 等. 基于双曲正切函数的光伏/温差自适应MPPT控制策略研究[J]. 农业工程学报, 2021, 37(16): 184-191.
WANG L S, BAI L, FANG J L, et al.Self-adaptive photovoltaic/temperature difference MPPT control strategy based on hyperbolic tangent function[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(16): 184-191.
[7] 石季英, 凌乐陶, 薛飞, 等. 基于自适应种群粒子群的光伏全局MPPT研究[J]. 电力电子技术, 2017, 51(5): 27-30.
SHI J Y, LING L T, XUE F, et al.Global MPPT study of PV system based on APPSO[J]. Power electronics, 2017, 51(5): 27-30.
[8] 张永革, 石季英, 张文, 等. 复杂遮阴条件下光伏系统MPPT控制改进PSO算法仿真研究[J]. 中国电机工程学报, 2014, 34(S1): 39-46.
ZHANG Y G, SHI J Y, ZHANG W, et al.Simulation study on improved PSO algorithm for MPPT control of photovoltaic system under complex shading conditions[J]. Proceedings of the CSEE, 2014, 34(S1): 39-46.
[9] 葛传九, 武鹏, 董祥祥, 等. 基于布谷鸟算法的光伏MPPT改进[J]. 太阳能学报, 2022, 43(10): 59-64.
GE C J, WU P, DONG X X, et al.Improved photovoltaic maximum power point tracking based on cuckoo search algorithm[J]. Acta energiae solaris sinica, 2022, 43(10): 59-64.
[10] 聂晓华, 王薇. 混沌改进猫群算法及其在光伏MPPT中的应用[J]. 中国电机工程学报, 2016, 36(22): 6103-6110.
NIE X H, WANG W.Chaos improved cat swarm optimization and its application in the PV MPPT[J]. Proceedings of the CSEE, 2016, 36(22): 6103-6110.
[11] 吴玲, 张秀锦, 刘秋华, 等. 基于多元宇宙优化算法的光伏发电MPPT控制算法[J]. 太阳能学报, 2023, 44(9): 204-211.
WU L, ZHANG X J, LIU Q H, et al.MPPT control algorithm of photovoltaic power generation based on multi-verse optimization algorithm[J]. Acta energiae solaris sinica, 2023, 44(9): 204-211.
[12] 薛飞, 马鑫, 田蓓, 等. 基于改进蜻蜓算法的光伏全局最大功率追踪[J]. 中国电力, 2022, 55(2): 131-137.
XUE F, MA X, TIAN B, et al.Photovoltaic global maximum power tracking based on improved dragonfly algorithm[J]. Electric power, 2022, 55(2): 131-137.
[13] 毛明轩, 冯心营, 陈思宇, 等. 基于贝叶斯优化卷积神经网络的路面光伏阵列最大功率点电压预测方法[J]. 中国电机工程学报, 2024, 44(2): 620-631.
MAO M X, FENG X Y, CHEN S Y, et al.A novel maximum power point voltage forecasting method for pavement photovoltaic array based on Bayesian optimization convolutional neural network[J]. Proceedings of the CSEE, 2024, 44(2): 620-631.
[14] 李艳波, 李林宜, 刘维宇, 等. 基于TSO-MSMA算法在光伏系统MPPT中的研究[J]. 太阳能学报, 2023, 44(8): 324-330.
LI Y B, LI L Y, LIU W Y, et al.Research on photovoltaic system MPPT based on TSO-MSMA algorithm[J]. Acta energiae solaris sinica, 2023, 44(8): 324-330.
[15] 李红岩, 王磊, 安平娟, 等. 基于改进黏菌算法的局部遮阴下光伏MPPT研究[J]. 太阳能学报, 2023, 44(10): 129-134.
LI H Y, WANG L, AN P J, et al.Study on photovoltaic MPPT under local shade based on improved slime mold algorithm[J]. Acta energiae solaris sinica, 2023, 44(10): 129-134.
[16] MUZATHIK A M, LANKA S.Photovoltaic modules operating temperature estimation using a simple correlation[J]. International journal of energy engineering, 2014, 4(4): 151-158.
[17] DENG L Y, LIU S Y.Snow ablation optimizer: a novel metaheuristic technique for numerical optimization and engineering design[J]. Expert systems with applications, 2023, 225: 120069.
基金
天津市研究生科研创新项目（2022BKY208）
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