摘要
光伏模型参数的辨识对于光伏系统的仿真、评估和控制至关重要。尽管已经提出了许多方法,但实现快速、准确、可靠的参数辨识仍然具有挑战性。提出了一种基于哈里斯鹰优化(HHO)和改进Nelder-Mead单纯形(INMS)的新型混合算法(HHO-INMS),用于实现光伏模型参数的辨识。HHO-INMS结合了HHO强大的全局探索和INMS强大的局部开发的优点,克服了HHO计算量大、易陷入局部最小值的缺点。并且使用单纯形的主对角线向量代替最差顶点来改进原NMS的收缩操作,进一步增强了收敛性。在文献中常用的R.T.C法国太阳能电池和Photowatt-PWP201光伏组件的实验数据集上,HHO-INMS与一些最新的算法进行了比较。实验结果表明HHO-INMS各方面优于其他对比算法,尤其是在收敛性方面,并且在算法时间消耗上也取得了最好的效果。
The identification of photovoltaic model parameters is crucial for the simulation,evaluation and control of photovoltaic systems.Although many methods have been proposed,achieving fast,accurate,and reliable parameter identification is still challenging.This paper proposes a new hybrid algorithm(HHO-INMS)based on Harris Hawk optimization(HHO)and improved Nelder-Mead simplex(INMS)to realize the identification of photovoltaic model parameters.HHO-INMS combines the advantages of HHO′s powerful global exploration and INMS′s powerful local development,overcoming the disadvantages of HHO′s large computational complexity and easy falling into local minima.And the main diagonal vector of the simplex is used instead of the worst vertex to improve the contraction operation of the original NMS,further enhancing the convergence.HHO-INMS is compared with some state-of-the-art algorithms on experimental data sets of R.T.C French solar cells and Photowatt-PWP201 photovoltaic modules commonly used in the literature.Experimental results show that HHO-INMS is superior to other comparative algorithms in all aspects,especially in terms of convergence,and also achieves the best results in terms of algorithm time consumption.
作者
钟胜铨
陈志聪
ZHONG Sheng-quan;CHEN Zhi-cong(College of Physics and Information Engineering,Fuzhou University,Fuzhou 350108,China)
出处
《电气开关》
2025年第4期26-31,35,共7页
Electric Switchgear
基金
国家自然科学基金项目(62271151)
福建省科技厅自然科学基金面上项目(2021J01580)。
