A Novel MPPT Method Based on Cuckoo Search Algorithm and Golden Section Search Algorithm for Partially Shaded PV System

Dimas Aji Nugraha, K. Lian, Suwarno
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引用次数: 12

Abstract

Cuckoo Search (CS) is a new optimization algorithm based on meta-heuristic (MH) approach. It has been used to solve optimization problem in many applications including Maximum Power Point Tracking (MPPT) problem. MH based algorithm is usually deployed to encounter a multiple peak problem in partially shaded centralized Photovoltaic (PV) system. CS algorithm performs a good result in tracking the Global Maximum Power Point (GMPP) compared to other MH based algorithms. However it still requires a relatively long tracking time to locate the Global Maximum Power Point (GMPP). This paper proposes a new MPPT algorithm by combining CS algorithm with Golden Section Search (GSS) algorithm to cultivate beneficial features from both algorithms. To validate the proposed algorithm, it will be evaluated by using various cases of partial shading. The result shows a noticeable performance improvement compared to original CS algorithm and other MH algorithm.
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基于布谷鸟搜索算法和黄金分割搜索算法的部分遮阳光伏系统MPPT新方法
布谷鸟搜索(Cuckoo Search, CS)是一种基于元启发式(meta-heuristic, MH)方法的新型优化算法。它已被用于解决许多应用中的优化问题,包括最大功率点跟踪(MPPT)问题。在部分遮阳集中式光伏系统中,通常采用基于MH的算法来解决多峰问题。与其他基于MH的算法相比,CS算法在跟踪全局最大功率点(GMPP)方面表现良好。然而,定位全局最大功率点(GMPP)仍然需要较长的跟踪时间。本文提出了一种新的MPPT算法,将CS算法与黄金分割搜索(Golden Section Search, GSS)算法相结合,从两种算法中挖掘出有益的特征。为了验证所提出的算法,将通过使用不同的部分遮阳情况来评估它。结果表明,与原有的CS算法和其他MH算法相比,该算法的性能有了明显的提高。
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