An Enhanced Moth-Flame Optimization with Multiple Flame Guidance Mechanism for Parameter Extraction of Photovoltaic Models

Zhenyu Wang, Zijian Cao, Chen Liu, Haowen Jia, Feng Tian, Fuxi Liu and Diego Oliva

Mathematical Problems in Engineering, 2022, vol. 2022, 1-15

Abstract: How to accurately and efficiently extract photovoltaic (PV) model parameters is the primary problem of photovoltaic system optimization. To accurately and efficiently extract the parameters of PV models, an enhanced moth-flame optimization (EMFO) with multiple flame guidance mechanism is proposed in this study. In EMFO, an adaptive flame number updating mechanism is used to adaptively control the flame number, which enhances the local and global exploration capabilities of MFO. Meanwhile, a multiple flame guidance mechanism is designed for the full use of the position information of flames, which enhances the global diversity of the population. The EMFO is evaluated with other variants of the MFO on 25 benchmark functions of CEC2005, 28 functions of CEC2017, and 5 photovoltaic model parameter extraction problems. Experimental results show that the EMFO has obtained a better performance than other compared algorithms, which proves the effectiveness of the proposed EMFO. The method proposed in this study provides MFO researchers with ideas for adaptive research and making full use of flame population information.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8398768

DOI: 10.1155/2022/8398768

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