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.

中文翻译：

---

一种基于布谷鸟搜索算法和黄金分割搜索算法的局部阴影光伏系统MPPT新方法

局部遮光是光伏（PV）系统中常见且难以解决的问题。已经采取了许多努力来缓解这个问题。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光伏系统的多峰 $P - V$ 曲线。布谷鸟搜索（CS）是一种基于 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。CS算法在跟踪全局最大功率点（GMPP）方面表现良好。然而，就像任何其他 MH 算法一样，它们的准确性和找到 GMPP 所需的跟踪时间之间仍然存在两难的权衡。本文提出了一种新的 MPPT 算法，将 CS 算法与黄金分割搜索 (GSS) 相结合，从这两种算法中获取有益的特征。为了验证所提出的算法，它在不同的部分着色情况下进行了评估。仿真和实验结果表明，与原始 CS 算法和其他 MH 算法相比，性能有了显着的提升。