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Orthogonal Nelder-Mead moth flame method for parameters identification of photovoltaic modules

机译：光伏模块参数识别的正交NELDER-MEAD飞蛾法

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摘要

Defining the optimal parameters of the photovoltaic system (PV) models according to the actual real voltage and current data is a crucial process during designing, emulating, estimating, dominating, and optimizing photovoltaic systems. Therefore, it is necessary to effectively advance the optimal parameters of the models based on the proper optimization methods. For this purpose, this paper proposes an orthogonal moth flame optimization (MFO) with a local search for identifying parameters of photovoltaic cell models, which is named NMSOLMFO. The presented method is organized based on the principal exploratory and exploitative mechanisms of MFO. Also, its exploration and exploitation capability is strengthened by the orthogonal learning (OL) strategy and Nelder-Mead simplex (NMS) method, and this new scheme supports a more stable equilibrium between the central propensities. In the new MFO-based method, OL strategy can construct a healthier candidate location for the inferior agents, and then, it directs them to probe a reasonable prospective zone throughout a few rational trials. Besides, the NMS local search scheme can augment the accurateness of the global optimal solution by searching its neighborhood throughout the searching process, and the global optimum is taken as the initial point. In our study, first, the developed MFO-based approach is employed to tackle IEEE CEC 2014 benchmark cases with 30D to evaluate the effectiveness of the method in solving high dimensional and multimodal problems. Then, it is utilized to deal with parameters identification of single diode model (SDM), double diode model (DDM), and photovoltaic module model (PVM). The results and statistical studies indicate that NMSO-LMFO can outperform the majority of other investigated methods concerning accuracy and convergence rapidity. The obtained results imply that the novel approach can provide a new practical tool for parameter definition in PV models, and it can be beneficial to upgrade the PV systems.
机译：根据实际实际电压和电流数据定义光伏系统（PV）型号的最佳参数是设计，模拟，估算，定制和优化光伏系统期间的重要过程。因此，有必要基于适当的优化方法有效地推进模型的最佳参数。为此目的，本文提出了一个正交的飞蛾火焰优化（MFO），具有本地搜索识别光伏电池模型的参数，该参数被命名为Nmsolmfo。本方法是根据MFO的主要探索和利用机制组织的。此外，其勘探和开发能力由正交学习（OL）策略和NELDER-MED SIMPLEX（NMS）方法加强，并且该新方案支持在中央施力之间更稳定的均衡。在基于新的MFO的方法中，OL策略可以为下级代理构建一个更健康的候选地点，然后，它指示它们在整个合理试验中探讨合理的未来区域。此外，NMS本地搜索方案可以通过在整个搜索过程中搜索其邻域来增强全局最佳解决方案的准确性，并且将全局最佳选择作为初始点。在我们的研究中，首先，采用开发的基于MFO的方法来解决IEEE CEC 2014基准案例，其中30D用于评估求解高维和多模式问题的方法的有效性。然后，利用它来处理单二极管模型（SDM），双二极管模型（DDM）和光伏模块模型（PVM）的参数识别。结果和统计研究表明，NMSO-LMFO可以优于大多数其他研究方法的准确性和收敛性。所获得的结果意味着新颖的方法可以为光伏型号中的参数定义提供新的实用工具，升级光伏系统可能是有益的。

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