全球能源互联网（英文）2022，Vol.5Issue(6)：627-644,18.DOI:10.1016/j.gloei.2022.12.005
基于布谷鸟搜索-灰狼优化的光伏系统多种运行条件最大功率点跟踪混合算法
Hybrid MPPT approach using Cuckoo Search and Grey Wolf Optimizer for PV systems under variant operating conditions
吉南·阿卜杜哈桑·萨利姆 1巴拉·阿尔巴克 1穆瓦法克· 希亚·阿尔万 1哈萨努扎曼1
作者信息
摘要
Abstract
Photovoltaic (PV) systems are adversely affected by partial shading and non-uniform conditions. Meanwhile, the addition of a bypass shunt diode to each PV module prevents hotspots. It also produces numerous peaks in the PV array's power-voltage characteristics, thereby trapping conventional maximum power point tracking (MPPT) methods in local peaks. Swarm optimization approaches can be used to address this issue. However, these strategies have an unreasonably long convergence time. The Grey Wolf Optimizer (GWO) is a fast and more dependable optimization algorithm. This renders it a good option for MPPT of PV systems operating in varying partial shading. The conventional GWO method involves a long conversion time, large steady-state oscillations, and a high failure rate. This work attempts to address these issues by combining Cuckoo Search (CS) with the GWO algorithm to improve the MPPT performance. The results of this approach are compared with those of conventional MPPT according to GWO and MPPT methods based on perturb and observe (P&O). A comparative analysis reveals that under non-uniform operating conditions, the hybrid GWO CS (GWOCS) approach presented in this article outperforms the GWO and P&O approaches.关键词
布谷鸟搜索方法/灰狼优化最大功率点跟踪算法/混合最大功率点跟踪方法/光伏系统/罗氏直流-直流变换器Key words
Cuckoo Search/GWO MPPT/Hybrid MPPT/PV system/Luo DC-DC converter引用本文复制引用
吉南·阿卜杜哈桑·萨利姆,巴拉·阿尔巴克,穆瓦法克· 希亚·阿尔万,哈萨努扎曼..基于布谷鸟搜索-灰狼优化的光伏系统多种运行条件最大功率点跟踪混合算法[J].全球能源互联网（英文）,2022,5(6):627-644,18.
