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首页> 外文期刊>Canadian journal of electrical and computer engineering >A Novel MPPT Method Based on Cuckoo Search Algorithm and Golden Section Search Algorithm for Partially Shaded PV System
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A Novel MPPT Method Based on Cuckoo Search Algorithm and Golden Section Search Algorithm for Partially Shaded PV System

机译：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

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摘要

Partial shading is a common and difficult problem to be solved in a photovoltaic (PV) system. Numerous efforts have been introduced to mitigate this problem. Some commonly used approaches are deploying some metaheuristic (MH) algorithm to track the multiple-peak P-V curve of partially shaded PV system. Cuckoo search (CS) is a new optimization algorithm based on the MH approach. It has been used to solve an optimization problem in many applications, including the maximum power point tracking (MPPT) problem. The CS algorithm performs well in tracking the global maximum power point (GMPP). However, just like any other MH algorithm, there is still a dilemmatic trading between their accuracy and the tracking time needed to find GMPP. This paper proposes a new MPPT algorithm by combining the CS algorithm with golden section search (GSS) to take beneficial features from both the algorithms. To validate the proposed algorithm, it is evaluated with various cases of partial shading. The simulation and experimental results show a noticeable performance improvement compared with the original CS algorithm and other MH algorithms.
机译：部分阴影是在光伏（PV）系统中要解决的常见且困难的问题。已经采取了许多努力来减轻这个问题。一些常用的方法是部署一些元启发式（MH）算法来跟踪部分阴影PV系统的多峰P-V曲线。布谷鸟搜索（CS）是基于MH方法的一种新的优化算法。它已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。 CS算法在跟踪全局最大功率点（GMPP）方面表现良好。但是，就像其他任何MH算法一样，它们的准确性与找到GMPP所需的跟踪时间之间仍然存在两难的折衷。本文提出了一种新的MPPT算法，它将CS算法与黄金分割搜索（GSS）相结合，以从这两种算法中受益。为了验证所提出的算法，在部分阴影的各种情况下对其进行了评估。仿真和实验结果表明，与原始CS算法和其他MH算法相比，性能有了显着提高。

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