An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions
An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions
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DOI：
10.3390/en14040953
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发表时间：
2021-02-01
期刊：
影响因子：
3.2
通讯作者：
Eltamaly, Ali M.
中科院分区：
文献类型：
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作者：
Eltamaly, Ali M.
部分遮光问题严重影响光伏系统的性能。在每个光伏组件上并联一个旁路二极管可以避免热点现象，但会导致光伏阵列的功率-电压（P-V）特性出现多个峰值，这会导致传统的最大功率点跟踪（MPPT）技术陷入局部峰值。这个问题迫使研究人员寻找智能技术来跟踪全局峰值，并防止在局部峰值收敛的可能性。群优化技术已被用来填补这一缺陷，然而，不幸的是，这些技术遭受不可接受的长收敛时间。布谷鸟搜索（CS）是最快和最可靠的优化技术之一，使其成为动态部分遮光条件下光伏系统最大功率点跟踪的理想选择。标准CS算法具有转换时间长、故障率高、稳态时振荡大等问题，本文旨在克服这些问题，并通过提高CS算法的性能来填补这一研究空白。从这种技术得到的结果进行比较，五个群优化技术。对比研究表明，本文提出的改进CS策略优于其他群优化技术。
The problem of partial shading has serious effects on the performance of photovoltaic (PV) systems. Adding a bypass diode in shunt to each PV module avoids hot-spot phenomena, but causes multi-peaks in the power-voltage (P-V) characteristics of the PV array, which cause traditional maximum power point tracking (MPPT) techniques to become trapped in local peaks. This problem has forced researchers to search for smart techniques to track global peaks and prevent the possibility of convergence at local peaks. Swarm optimization techniques have been used to fill this shortcoming; unfortunately, however, these techniques suffer from unacceptably long convergence time. Cuckoo search (CS) is one of the fastest and most reliable optimization techniques, making it an ideal option to be used as an MPPT of PV systems under dynamic partial shading conditions. The standard CS algorithm has a long conversion time, high failure rate, and high oscillations at steady state; this paper aims to overcome these problems and to fill this research gap by improving the performance of the CS. The results obtained from this technique are compared to five swarm optimization techniques. The comparison study shows the superiority of the improved CS strategy introduced in this paper over the other swarm optimization techniques.