基于蜣螂优化算法的光伏电池参数辨识  

Parameters Identification of Photovoltaic Cells Based on Improved Dung Beetle Optimization Algorithm

作　　者：郑修斌 陈珺[1] ZHENG Xiubin;CHEN Jun(Key Laboratory of Advanced Process Control in Light Industry,Ministry of Education(Jiangnan University),Wuxi Jiangsu 214122,China)

机构地区：[1]轻工过程先进控制教育部重点实验室(江南大学),江苏无锡214122

出　　处：《广西师范大学学报（自然科学版）》2024年第4期51-63,共13页Journal of Guangxi Normal University:Natural Science Edition

基　　金：国家自然科学基金(62073154)。

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。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 0.000986,0.000983 and 0.002425.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.

关 键 词：光伏电池 参数辨识 蜣螂优化算法 元启发式算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM914.4[自动化与计算机技术—控制科学与工程]