利用迄今最佳ABC算法提取光伏组件参数

E. Garoudja, W. Filali
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引用次数: 1

摘要

在本工作中,采用了一种受自然启发的算法,这是迄今为止最好的人工蜂群算法,用于提取光伏(PV)组件的电气参数。该算法模拟了蜜蜂在自然界中寻找食物来源的行为,以识别一个二极管模型(ODM)参数。通过对LG395N2W光伏模块在两种工况下的仿真得到的两种类型的电特性(I-V和P-V),验证了我们策略的有效性。最后,与其他启发式算法粒子群优化(PSO)算法进行了比较研究。结果表明,目前最好的ABC算法在参数精度、适应度值和收敛速度上明显优于PSO算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Photovoltaic module parameters extraction using best-so-far ABC algorithm
In the present work, a nature inspired algorithm, which is the best-so-far Artificial Bee Colony algorithm, has been used to make the extraction of the electrical parameters of a Photovoltaic (PV) module. This algorithm emulates the behavior of bees in nature, where they search their food sources, to identify the one diode model (ODM) parameters. The effectiveness of our strategy has been checked by using two types of electrical characteristics (I-V and P-V) obtained from the simulation of LG395N2W PV module at two operating conditions. Finally, a comparative study has been elaborated with other heuristic algorithm, Particle Swarm Optimization (PSO) algorithm. Results show clearly that the best-so-far ABC noticeably outperforms PSO in the parameters accuracy, fitness value and convergence rate.
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