A Novel Technique Based on Peafowl Optimization Algorithm for Maximum Power Point Tracking of PV Systems Under Partial Shading Condition

被引:3
作者
Li, Dongrui [1 ]
Li, Jinjin [2 ]
Wang, Ning [3 ]
机构
[1] Yunnan Baichuan Power Technol Co Ltd, Kunming, Yunnan, Peoples R China
[2] CISDI Elect Technol Co Ltd, Chongqing, Peoples R China
[3] Guian New Dist Elect Distribut Co Ltd, Guiyang, Peoples R China
关键词
PV system; MPPT; partial shading condition; POA algorithm; matlab; simulink; PHOTOVOLTAIC SYSTEM;
D O I
10.3389/fenrg.2021.801571
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
One of the most critical tasks during the application of photovoltaic (PV) systems is to harvest the optimal output power at various environmental scenarios, which is called maximum power point tracking (MPPT). Though plenty of advanced techniques are developed to achieve this purpose, most of them have corresponding prominent disadvantages, such as inefficient tracking ability, high computation burden, and complex convergence mechanism. Therefore, this work aims to propose a novel and powerful bio-inspired meta-heuristic optimization algorithm called peafowl optimization algorithm (POA), which is inspired by the group food searching behaviors of peafowl swarm. It can effectively achieve a suitable balance between local exploitation and global exploration thanks to its efficient exploratory and exploitative searching operators. Thus, a satisfactory MPPT performance for PV systems under partial shading condition (PSC) can be obtained based on POA. Moreover, two case studies, e.g., start-up test and step change in solar irradiation with constant temperature, are adopted to fairly and comprehensively validate the superiority and effectiveness of POA in contrast with particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), respectively.
引用
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页数:10
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