A novel Elite Opposition-based Jaya algorithm for parameter estimation of photovoltaic cell models
A novel Elite Opposition-based Jaya algorithm for parameter estimation of photovoltaic cell models
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DOI：
10.1016/j.ijleo.2017.10.081
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发表时间：
2018-02
期刊：
影响因子：
3.1
通讯作者：
Long Wang;Chao Huang
中科院分区：
文献类型：
--
作者：
Long Wang;Chao Huang
本文研究了基于精英反对的Jaya算法在光伏电池模型参数估计中的应用。EO-Jaya算法是一种没有算法特定参数的群体智能算法。与一般的Jaya算法相比，精英反对学习策略被纳入到解决方案的更新阶段，这增加了解决方案的多样性。通过对一个真实的光伏电池模型参数的估计，验证了EO-Jaya算法的有效性。计算结果证明了该算法的优越性相比，新公布的估计方法。
Parameter estimation of photovoltaic (PV) cell models using a novel Elite Opposition-based Jaya (EO-Jaya) algorithm is studied in this letter. The EO-Jaya is a swarm intelligence algorithm without algorithm specific parameters. Compared with the generic Jaya algorithm, the Elite Opposition Learning strategy is incorporated into the solution updating phase, which increases the solution diversity. The effectiveness of the EO-Jaya algorithm is validated via estimating model parameters of a real PV cell. The computational results prove the superiority of the proposed algorithm compared to newly published estimation methods.