基于蜣螂优化算法的光伏电池参数辨识

doi:10.16088/j.issn.1001-6600.2023091602

摘要/Abstract

摘要： 为解决当前光伏电池参数辨识精度低、速度慢、稳定性较差等问题,本文引入Tent混沌映射初始化种群,使初始解尽可能均匀地分布在解空间内;加入Levy飞行策略,更新蜣螂滚球行为时的个体位置,跳出局部最优解,扩大搜索范围;采用自适应t分布和动态选择策略,在更新蜣螂位置时使用以迭代次数为自由度参数的t分布变异算子对进行扰动,增强算法的全局开发能力和局部探索能力,加快收敛速度;提出一种基于蜣螂优化算法的光伏电池参数辨识方法。实验结果表明,对RTC France的单二极管模型、双二极管模型和光伏组件Photowatt-PWP 201模型进行参数辨识,获得的均方根误差分别为0.000 986、0.000 983、0.002 425。本文提出的方法可以更快更精确地辨识出光伏电池参数,且误差小,具有较高的稳定性。

关键词: 光伏电池, 参数辨识, 蜣螂优化算法, 元启发式算法

Abstract: At present, there are some problems in parameter identification of photovoltaic cells, such as low precision, slow speed and poor stability, which need to be improved. To solve these problems, a method of parameter identification of photovoltaic cells based on dung beetle optimization algorithm is proposed in this paper. By introducing Tent chaotic mapping to initialize the population, the initial solution is distributed as evenly as possible in the solution space. Levy flight strategy is added to update individual position of dung beetle during ball rolling, jump out of the local optimal solution, and expand the search scope. The adaptive T-distribution and dynamic selection strategies are adopted, and the T-distribution mutation operator with the number of iterations as the degree of freedom parameter is used to perturbate the dung beetle position, which enhances the global development ability and local exploration ability of the algorithm, and accelerates the convergence speed. The experimental results show that the root-mean square errors of RTC France’s single diode model, double diode model and Photowatt-PWP 201 model are respectively 0.000 986, 0.000 983 and 0.002 425. The method proposed in this paper can identify the parameters of photovoltaic cells faster and more accurately, and has a small error and high stability.

Key words: photovoltaic cells, parameter identification, dung beetle optimization algorithm, meta-heuristic algorithm

中图分类号: TP18;TM914.4

郑修斌, 陈珺. 基于蜣螂优化算法的光伏电池参数辨识[J]. 广西师范大学学报（自然科学版）, 2024, 42(4): 51-63.

ZHENG Xiubin, CHEN Jun. Parameters Identification of Photovoltaic Cells Based on Improved Dung Beetle Optimization Algorithm[J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(4): 51-63.

参考文献

[1] NAZEERUDDIN M K. In retrospect: Twenty-five years of low-cost solar cells[J]. Nature, 2016, 538(7626): 463-464. DOI: 10.1038/538463a.
[2] 王一凡,王辉,李旭阳,等.电氢混合储能微电网容量配置优化的研究综述[J].广西师范大学学报(自然科学版),2022,40(6):18-36.DOI: 10.16088/j.issn.1001-6600.2022011901.
[3] 杨悦强,祝龙记.微电网超级电容器混合储能系统控制策略[J].广西师范大学学报(自然科学版),2021,39(2):71-80.DOI: 10.16088/j.issn.1001-6600.2020050301.
[4] 周俊宇,邱桂华,陆家比.光伏接入配电网调峰优化调度控制方法研究[J].电子设计工程,2023,31(24):122-126.DOI: 10.14022/j.issn1674-6236.2023.24.026.
[5] 菅荣飞,邢关生,李振伟.一种基于双环LADRC的混合储能变换器控制方法[J].电子设计工程,2024,32(12):41-48.DOI: 10.14022/j.issn1674-6236.2024.12.009.
[6] 范海花,尚玉玲.基于二维卷积神经网络的模拟电路故障诊断方法[J].桂林电子科技大学学报,2023,43(6):493-500.DOI: 10.16725/j.cnki.cn45-1351/tn.2023.06.008.
[7] 谈恩民,李莹.基于SFO优化SELM的模拟电路故障诊断[J].桂林电子科技大学学报,2023,43(6):501-508.DOI: 10.16725/j.cnki.cn45-1351/tn.2023.06.001.
[8] 王一鸣,许颇,张凌翔,等.基于SSA-SVM的光伏阵列故障诊断模型[J].电子设计工程,2024,32(13):46-49.DOI: 10.14022/j.issn1674-6236.2024.13.010.
[9] LUO J K, HE F Z, GAO X X. An enhanced grey wolf optimizer with fusion strategies for identifying the parameters of photovoltaic models[J]. Integrated Computer-Aided Engineering, 2023, 30(1): 89-104. DOI: 10.3233/ICA-220693.
[10] CHEN H L, JIAO S, HEIDARI A A, et al. An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models[J]. Energy Conversion and Management, 2019, 195: 927-942. DOI: 10.1016/j.enconman.2019.05.057.
[11] YE X J, LIU W, LI H, et al. Modified whale optimization algorithm for solar cell and PV module parameter identification[J]. Complexity, 2021, 2021: 8878686. DOI: 10.1155/2021/8878686.
[12] HUANG Z Y, CHEN L M, LI M, et al. A multiple learning moth flame optimization algorithm with probability-based chaotic strategy for the parameters estimation of photovoltaic models[J]. Journal of Renewable and Sustainable Energy, 2021, 13(4): 043502. DOI: 10.1063/5.0048961.
[13] XUE J K, SHEN B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing, 2023, 79(7): 7305-7336. DOI: 10.1007/s11227-022-04959-6.
[14] ZHANG R Z, ZHU Y J. Predicting the mechanical properties of heat-treated woods using optimization-algorithm-based BPNN[J]. Forests, 2023, 14(5): 935. DOI: 10.3390/f14050935.
[15] HU T Y, ZHANG H, ZHOU J T. Prediction of the debonding failure of beams strengthened with FRP through machine learning models[J]. Buildings, 2023, 13(3): 608. DOI: 10.3390/buildings13030608.
[16] LING G B, WANG Z W, SHI Y K, et al. Membrane fouling prediction based on Tent-SSA-BP[J]. Membranes, 2022, 12(7): 691. DOI: 10.3390/membranes12070691.
[17] 杨宇伦,凌铭.基于改进鸡群优化算法的质子交换膜燃料电池模型参数辨识[J].太阳能学报,2023,44(2):269-278.DOI: 10.19912/j.0254-0096.tynxb.2021-1138.
[18] YANG X X, LIU J, LIU Y, et al. A novel adaptive sparrow search algorithm based on chaotic mapping and T-distribution mutation[J]. Applied Sciences, 2021, 11(23): 11192. DOI: 10.3390/app112311192.
[19] 吴忠强,谢宗奎,刘重阳,等.基于混沌搜索的改进狮群算法及其在光伏电池参数辨识中的应用[J].计量学报,2021,42(4):415-423.DOI: 10.3969/j.issn.1000-1158.2021.04.03.
[20] HOUSSEIN E H, NAGEH G, ABD ELAZIZ M, et al. An efficient Equilibrium Optimizer for parameters identification of photovoltaic modules[J]. PeerJ. Computer Science, 2021, 7: e708. DOI: 10.7717/peerj-cs.708.
[21] ADEDIPUPO O O, AYODELE A A, ABIDEEN L T, et al. Detection of appropriate model for Nigeria population growth using root mean square error (RMSE)[J]. International Journal of Systems Science and Applied Mathematics, 2022, 7(3): 46-51. DOI: 10.11648/j.ijssam.20220703.11.
[22] LI Y C, HAN M X, GUO Q L. Modified whale optimization algorithm based on tent chaotic mapping and its application in structural optimization[J]. KSCE Journal of Civil Engineering, 2020, 24(12): 3703-3713. DOI: 10.1007/s12205-020-0504-5.
[23] GOWRI V, BARANIDHARAN B. An energy efficient and secure model using chaotic levy flight deep Q-learning in healthcare system[J]. Sustainable Computing: Informatics and Systems, 2023, 39: 100894. DOI: 10.1016/j.suscom.2023.100894.
[24] 胡竞杰,储昭碧,郭愉乐,等.基于自适应t分布与动态权重的樽海鞘群算法[J].计算机应用研究,2023,40(7):2068-2074.DOI: 10.19734/j.issn.1001-3695.2022.11.0628.
[25] 沙梦洲,沈韬,曾凯,等.融合深浅特征和动态选择机制的行人检测研究[J].数据采集与处理,2023,38(1):162-173.DOI: 10.16337/j.1004-9037.2023.01.014.
[26] XU S H, WANG Y. Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm[J]. Energy Conversion and Management, 2017, 144: 53-68. DOI: 10.1016/j.enconman.2017.04.042.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed