利用蜜獾和遗传算法实现部分遮阳下光伏最大功率跟踪的混合方法

IF 3 4区工程技术 Q3 ENERGY & FUELS

Energies Pub Date : 2024-08-08 DOI:10.3390/en17163935

Zhi-Kai Fan, Annisa Setianingrum, K. Lian, Suwarno Suwarno

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引用次数: 0

摘要

本研究提出了一种结合蜜獾算法（HBA）和遗传算法（GA）的最大功率点跟踪（MPPT）新方法。整合的目的是优化部分遮阳条件（PSCs）下的光伏（PV）系统性能。最初，HBA 用于广泛探索和识别潜在的解决方案，同时避免局部最优。必要时，再利用 GA 通过选择、交叉和突变操作来摆脱局部最优状态。平均而言，与 HBA 相比，该建议方法的跟踪时间缩短了 40%，效率提高了 0.77%。在动态情况下，建议的方法比 HBA 提高了 4.81%。

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A Hybrid Approach for Photovoltaic Maximum Power Tracking under Partial Shading Using Honey Badger and Genetic Algorithms

This study presents a new approach for Maximum Power Point Tracking (MPPT) by combining the honey badger algorithm (HBA) with a Genetic Algorithm (GA). The integration aims to optimize photovoltaic (PV) system performance in partial shading conditions (PSCs). Initially, the HBA is utilized to explore extensively and identify potential solutions while avoiding local optima. If necessary, the GA is then employed to escape local optima through selection, crossover, and mutation operations. On average, this proposed method has a 40% improvement in tracking time and 0.77% in efficiency compared with the HBA. In a dynamic case, the proposed method achieves a 4.81% improvement compared to HBA.

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来源期刊

Energies ENERGY & FUELS-

CiteScore

6.20

自引率

21.90%

发文量

8045

审稿时长

1.9 months

期刊介绍： Energies (ISSN 1996-1073) is an open access journal of related scientific research, technology development and policy and management studies. It publishes reviews, regular research papers, and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.