太阳电池模型参数的准确辨识对光伏组件功率预测和最大功率点追踪有较大影响,必须保证较高的辨识精度。传统的智能算法能做到一定程度上的参数辨识,但均存在精度不足、收敛速度慢、易陷入局部最优等问题。针对此类问题,提出基于改进鹈鹕优化算法（IPOA）的太阳电池模型参数辨识方法。该算法中种群个体联系紧密,通过随机性的互相学习进行位置更新,在工程应用领域有着较传统算法更好的效果。同时,针对该算法特点,引入基于Jaya算法的位置更新策略,使种群的候选解更趋向最优解;改进了递减因子,使模型在迭代中后期寻优效果更好。增加了莱维飞行策略,有效提高了算法精度。在不同的太阳辐照度条件下,IPOA都有较好效果,辨识结果与实际曲线拟合度高,表明IPOA能在不同环境中对太阳电池模型参数进行准确有效辨识。
Abstract
Accurate identification of solar cell model parameters has a great impact on PV module power prediction and maximum power point tracking, and it must be ensured to have high accuracy. Traditional intelligent algorithms can achieve a certain degree of parameter identification, but they all suffer from the problems of insufficient accuracy, slow convergence, and easy to fall into local optimality. To address such problems, a solar cell model parameter identification method based on the improved pelican optimization algorithm （IPOA） is proposed. In this algorithm, the population individuals are closely connected, and the position is updated by mutual learning of randomness, which has better effect than the traditional algorithm in engineering applications. At the same time, for the characteristics of this algorithm, a position updating strategy based on Jaya algorithm is introduced to make the candidate solutions of the population more optimal; the decreasing factor is improved to make the model better in the later stage of the iteration. The Lévy flight strategy is added, which effectively improves the algorithm accuracy. IPOA has good results under different solar irradiance, and the discrimination results fit well with the actual curves, indicating that IPOA can accurately and effectively identify the solar cell model parameters in different environments.
关键词
太阳电池 /
参数辨识 /
启发式算法 /
最优化 /
混沌初始化 /
IPOA
Key words
solar cells /
parameter identification /
heuristic algorithms /
optimization /
chaotic initialization /
IPOA
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] CRISTALDI L, FAIFER M, LAURANO C, et al.Model-based maximum power point tracking for photovoltaic panels: parameters identification and training database collection[J]. IET renewable power generation, 2020, 14(15): 2876-2884.
[2] 丁明, 王伟胜, 王秀丽, 等. 大规模光伏发电对电力系统影响综述[J]. 中国电机工程学报, 2014, 34(1): 1-14.
DING M, WANG W S, WANG X L, et al.A review on the effect of large-scale PV generation on power systems[J]. Proceedings of the CSEE, 2014, 34(1): 1-14.
[3] HACHANA O, TINA G M, HEMSAS K E.PV array fault diagnostictechnique for BIPV systems[J]. Energy and buildings, 2016, 126: 263-274.
[4] BATZELIS E I, PAPATHANASSIOU S A.A method for the analytical extraction of the single-diode PV model parameters[J]. IEEE transactions on sustainable energy, 2016, 7(2): 504-512.
[5] VILLALVA M G, GAZOLI J R, FILHO E R.Comprehensive approach to modeling and simulation of photovoltaic arrays[J]. IEEE transactions on power electronics, 2009, 24(5): 1198-1208.
[6] 张海宁. 基于非线性最小二乘法的光伏电池参数辨识[J]. 现代电力, 2017, 34(6): 79-84.
ZHANG H N.Parameter identification method for photovoltaic cells based on nonlinear least square method[J]. Modern electric power, 2017, 34(6): 79-84.
[7] EBRAHIMI S M, SALAHSHOUR E, MALEKZADEH M.Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm[J]. Energy, 2019, 179: 358-372.
[8] ISMAIL M S, MOGHAVVEMI M, MAHLIA T M I. Characterization of PV panel and global optimization of its model parameters using genetic algorithm[J]. Energy conversion and management, 2013, 73: 10-25.
[9] WANG L, HUANG C.A novel Elite Opposition-based Jaya algorithm for parameter estimation of photovoltaic cell models[J]. Optik, 2018, 155: 351-356.
[10] GUDE S, JANA K C.A multiagent system based cuckoo search optimization for parameter identification of photovoltaic cell using Lambert W-function[J]. Applied soft computing, 2022, 120: 108678.
[11] ABD ELAZIZ M, OLIVA D.Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm[J]. Energy conversion and management, 2018, 171: 1843-1859.
[12] PAN J W, GAO Y, QIAN Q, et al.Parameters identification of photovoltaic cells using improved version of the chaotic grey wolf optimizer[J]. Optik, 2021, 242: 167150.
[13] 莫仕勋, 杨皓, 蒋坤坪, 等. 基于改进秃鹰搜索算法的变压器J-A模型参数辨识[J]. 电工电能新技术, 2022, 41(4): 67-74.
MO S X, YANG H, JIANG K P, et al.Parameter identification of transformer J-A model based on improved BES algorithm[J]. Advanced technology of electrical engineering and energy, 2022, 41(4): 67-74.
[14] 吴忠强, 刘重阳. 基于IHHO算法的光伏电池工程模型的参数辨识[J]. 计量学报, 2021, 42(2): 221-227.
WU Z Q, LIU C Y.Parameter identification of photovoltaic cell engineering model based on IHHO algorithm[J]. Acta metrologica sinica, 2021, 42(2): 221-227.
[15] TROJOVSKÝ P, DEHGHANI M.Pelican optimization algorithm: a novel nature-inspired algorithm for engineering applications[J]. Sensors, 2022, 22(3): 855.
[16] WU Z Q, YU D Q, KANG X H.Parameter identification of photovoltaic cell model based on improved ant lion optimizer[J]. Energy conversion and management, 2017, 151: 107-115.
[17] 孙以泽, 彭乐乐, 孟婥, 等. 基于Lambert W函数的太阳电池组件参数提取及优化[J]. 太阳能学报, 2014, 35(8): 1429-1434.
SUN Y Z, PENG L L, MENG Z, et al.Parameters extraction and optimization for PV module based on Lambert W function[J]. Acta energiae solaris sinica, 2014, 35(8): 1429-1434.
[18] LAUDANI A, MANCILLA-DAVID F, RIGANTI-FULGINEI F, et al.Reduced-form of the photovoltaic five-parameter model for efficient computation of parameters[J]. Solar energy, 2013, 97: 122-127.
[19] YAO X, LIU Y, LIN G M.Evolutionary programming made faster[J]. IEEE transactions on evolutionary computation, 1999, 3(2): 82-102.
[20] NAEIJIAN M, RAHIMNEJAD A, EBRAHIMI S M, et al.Parameter estimation of PV solar cells and modules using Whippy Harris Hawks Optimization Algorithm[J]. Energy reports, 2021, 7: 4047-4063.
基金
天津市科技计划（18ZXYENC00100; 22ZYCGSN00190）
PDF(1951 KB)
