光伏系统
萃取(化学)
任务(项目管理)
计算机科学
工程类
化学
系统工程
电气工程
色谱法
作者
Jing Liang,Kangjia Qiao,Kunjie Yu,Boyang Qu,Shilei Ge,Yaxin Li,Guanlin Chen
标识
DOI:10.1016/j.enconman.2020.112509
摘要
Abstract As the demand for solar energy increases dramatically, the optimization and control of photovoltaic systems become increasingly important, accurate and reliable parameter identification of photovoltaic models is always required, which proposes an urgent need for accurate and robust algorithms. To this end, many heuristic algorithms have been proposed to extract the parameters of different photovoltaic models. However, they only extract the parameters of one model in a single run, which is inconsistent with the human ability to solve multiple tasks simultaneously and ignores the useful information derived from different models. Therefore, in this paper an evolutionary multi-task optimization algorithm is proposed to extract the parameters of multiple different photovoltaic models simultaneously. To be specific, the helpful information found by the population is transferred through the cross-task crossover to improve the performance in terms of solution quality and convergence rate of the population. The proposed algorithm is evaluated by extracting the parameters of three different models simultaneously, i.e., single diode, double diode, and photovoltaic module model. Comprehensive results demonstrate that the proposed algorithm has better performance with respect to the accuracy and robustness in comparison with other state-of-the-art algorithms.
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