数学优化
元启发式
计算机科学
趋同(经济学)
局部最优
光伏系统
理论(学习稳定性)
最优化问题
算法
排名(信息检索)
操作员(生物学)
早熟收敛
数学
粒子群优化
机器学习
工程类
电气工程
生物化学
化学
抑制因子
转录因子
经济
基因
经济增长
作者
Reda Mohamed,Mohamed Abdel‐Basset,Karam M. Sallam,Ibrahim M. Hezam,Ahmad M. Alshamrani,Ibrahim A. Hameed
标识
DOI:10.1038/s41598-024-52416-6
摘要
Abstract The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear optimization problem that cannot be accurately solved by traditional techniques. Therefore, metaheuristic algorithms have been recently used to solve this problem due to their potential to approximate the optimal solution for several complicated optimization problems. Despite that, the existing metaheuristic algorithms still suffer from sluggish convergence rates and stagnation in local optima when applied to tackle this problem. Therefore, this study presents a new parameter estimation technique, namely HKOA, based on integrating the recently published Kepler optimization algorithm (KOA) with the ranking-based update and exploitation improvement mechanisms to accurately estimate the unknown parameters of the third-, single-, and double-diode models. The former mechanism aims at promoting the KOA’s exploration operator to diminish getting stuck in local optima, while the latter mechanism is used to strengthen its exploitation operator to faster converge to the approximate solution. Both KOA and HKOA are validated using the RTC France solar cell and five PV modules, including Photowatt-PWP201, Ultra 85-P, Ultra 85-P, STP6-120/36, and STM6-40/36, to show their efficiency and stability. In addition, they are extensively compared to several optimization techniques to show their effectiveness. According to the experimental findings, HKOA is a strong alternative method for estimating the unknown parameters of PV models because it can yield substantially different and superior findings for the third-, single-, and double-diode models.
科研通智能强力驱动
Strongly Powered by AbleSci AI