基于IPOA的太阳电池模型参数辨识  

PARAMETER IDENTIFICATION OF SOLAR CELL MODEL BASED ON IPOA

作　　者：吴艳娟[1,2] 刘振朝 王云亮 Wu Yanjuan;Liu Zhenchao;Wang Yunliang(School of Electrical Engineering and Automation,Tianjin University of Technology,Tianjin 300384,China;Tianjin Key Laboratory of Complex System Control Theory and Application,Tianjin 300384,China)

机构地区：[1]天津理工大学电气工程与自动化学院,天津300384 [2]天津市复杂系统控制理论及应用重点实验室,天津300384

出　　处：《太阳能学报》2024年第1期1-10,共10页Acta Energiae Solaris Sinica

基　　金：天津市科技计划(18ZXYENC00100,22ZYCGSN00190)。

摘　　要：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。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 

分 类 号：TM615[电气工程—电力系统及自动化]