提出一种基于改进鹰栖息优化算法的最大功率追踪控制,算法首先对整个区域进行随机抽样,通过目标函数在抽样点寻找最优解,再对最优解进行二次抽样,从而实现从全局搜索到局部搜索的过渡。并在此基础上,引入适应值变量作为算法的反馈参数,实现了从全局搜索到局部搜索的自适应转变。仿真结果表明,与传统鹰栖息优化算法、布谷鸟算法及粒子群算法相比,所提算法在均匀光照,静态、动态局部阴影情况下均具有追踪速度快、收敛精度高和前期振荡小的特点,能有效提升光伏系统的最大功率跟踪效率和精度。
Abstract
In this paper, a maximum power tracking control based on the improved eagle perching optimization algorithm is proposed. The algorithm first randomly samples the whole area, finds the optimal solution at the sampling point through the objective function, and then samples the optimal solution twice, so as to realize the transition from global search to local search. On this basis, the adaptive variable is introduced as the feedback parameter of the algorithm, and the adaptive transformation from global search to local search is realized. The simulation results show that compared with the traditional eagle rooster optimization algorithm, Cuckoo algorithm and particle swarm optimization algorithm, the proposed algorithm has the characteristics of fast-tracking speed, high convergence accuracy and small early oscillation under uniform illumination, static and dynamic local shadow conditions, and can effectively improve the maximum power tracking efficiency and accuracy of photovoltaic systems.
关键词
光伏系统 /
最大功率追踪 /
光照条件 /
改进鹰栖息优化算法 /
局部阴影情况
Key words
photovoltaic system /
maximum power point tracking /
illumination conditions /
improved eagle perching optimization algorithm /
partial shadow condition
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] ISHAQUE K, SALAM Z, TAHERI H, et al.Modeling and simulation of photovoltaic (PV) system during partial shading based on a two-diode model[J]. Simulation modelling practice and theory, 2011, 19(7): 1613-1626.
[2] MOHANTY S, SUBUDHI B, RAY P K.A grey wolf-assisted perturb & observe MPPT algorithm for a PV system[J]. IEEE transactions on energy conversion, 2017, 32(1): 340-347.
[3] MOTAHHIR S, CHALH A, EL GHZIZAL A, et al.Development of a low-cost PV system using an improved INC algorithm and a PV panel Proteus model[J]. Journal of cleaner production, 2018, 204: 355-365.
[4] 钟彦平, 帅挽澜, 余笑侬, 等. 基于“国网芯” 的含光伏配电网保护研究[J]. 电气技术, 2019, 20(8): 126-130.
ZHONG Y P, SHUAI W L, YU X N, et al.Research on protection of distribution network containing photovoltaic based on “State Grid Chip”[J]. Electrical engineering, 2019, 20(8): 126-130.
[5] 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.
[6] 李昂, 刘文锋, 李音柯, 等. 基于IP&O-ICS算法的光伏系统MPPT控制研究[J]. 太阳能学报, 2023, 44(5): 203-209.
LI A, LIU W F, LI Y K, et al.Research on mppt control of photovoltaic system based on IP&O-ICS algorithm[J]. Acta energiae solaris sinica, 2023, 44(5): 203-209.
[7] 王盛洁. 鹰栖息优化算法的改进和应用研究[D]. 荆州: 长江大学, 2022.
WANG S J.Research on improvement and application of eagle habitat optimization algorithm[D]. Jingzhou: Yangtze University, 2022.
[8] 胡洁, 王盛洁, 张涛. 一种混合改进的鹰栖息优化算法[J]. 长江大学学报(自然科学版), 2022, 19(4): 111-118.
HU J, WANG S J, ZHANG T.A hybrid improved eagle perching optimization algorithm[J]. Journal of Yangtze University (natural science edition), 2022, 19(4): 111-118.
[9] 黄天昊. 局部阴影条件下光伏系统最大功率跟踪控制策略研究[D]. 南宁: 广西大学, 2019.
HUANG T H.Research on maximum power tracking control strategy of photovoltaic system under local shadows[D]. Nanning: Guangxi University, 2019.
[10] 丁爱华. 基于PSO的复杂环境下光伏MPPT控制系统研究[D]. 南宁: 广西大学, 2013.
DING A H.Research on photovoltaic MPPT control system in complex environment based on PSO[D]. Nanning: Guangxi University, 2013.
[11] CHALH A, MOTAHHIR S, EL GHZIZAL A, et al.Global MPPT of photovoltaic system based on scanning method under partial shading condition[J]. SN applied sciences, 2020, 2(4): 771.
[12] 杨亚, 张兰红, 谢生清. 基于改进黏菌优化算法的光伏MPPT方法[J]. 自动化与仪表, 2023, 38(3): 1-5.
YANG Y, ZHANG L H, XIE S Q.Photovoltaic MPPT method based on improved slime mold algorithm[J]. Automation & instrumentation, 2023, 38(3): 1-5.
[13] ZHENG D D, LI J, WU W, et al.The study for MPPT of photovoltaic system based on terminal sliding mode control method[J]. IOP conference series: earth and environmental science, 2020, 467(1): 012089.
[14] 易磊, 谢雨龙, 曾凡炎, 等. 基于神经网络的改进扰动观察法MPPT控制[J]. 太阳能学报, 2022, 43(4): 198-203.
YI L, XIE Y L, ZENG F Y, et al.Improved perturbation observation method mppt control based on neural network[J]. Acta energiae solaris sinica, 2022, 43(4): 198-203.
[15] 周睿, 徐良, 刘文浩, 等. 基于改进扰动观察法的光伏MPPT控制算法研究[J]. 电源技术, 2023, 47(3): 388-392.
ZHOU R, XU L, LIU W H, et al.Research on MPPT algorithm based on improved perturbation and observation method[J]. Chinese journal of power sources, 2023, 47(3): 388-392.
[16] 郑旭彬, 李梦达, 梁智超, 等. 局部遮荫下改进蝴蝶算法的MPPT控制仿真研究[J]. 计算机仿真, 2022, 39(1): 96-101.
ZHENG X B, LI M D, LIANG Z C, et al.MPPT control strategy of improved butterfly algorithm under partial shading[J]. Computer simulation, 2022, 39(1): 96-101.
基金
国家自然科学基金（51971128; 52171185）; 上海市优秀学术/技术带头人计划（20XD1401800）
PDF(6890 KB)
