总结了在部分遮阳条件下光伏系统全局最大功率跟踪的几种智能算法的基础上,提出了一种新的最大功率跟踪算法

Youjie Ma, Xuesong Zhou, Zhiqiang Gao, Tianqi Bai
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引用次数: 2

摘要

高效节能转换一直是光伏发电领域的研究热点。对比传统算法,智能算法在部分遮光影响下的多峰输出功率特性方面具有显著优势。本文介绍了一些流行的智能算法,如粒子群优化(PSO)、遗传算法(GA)、蚁群优化(ACO)等。当光伏系统受到部分遮阳的影响时,需要GMPPT(全局最大功率点跟踪)算法来提高系统的能量收集能力。通过对上述几种智能优化方法的搜索效率和收敛性的分析和讨论,仍然存在多重问题,通常不能准确、快速地跟踪GMPP。因此,本文指出提出几种混合智能算法来弥补单一算法的不足,并对其进行改进优化具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Summary of the novel MPPT (maximum power point tracking) algorithm based on few intelligent algorithms specialized on tracking the GMPP (global maximum power point) for photovoltaic systems under partially shaded conditions
Energy efficient conversion is always a hot topic in the field of photovoltaic power generation. Contract to traditional algorithm, the lasted research showed intelligent algorithms have remarkable advantages in multiple-peak output power characteristics affected by partial shading. This article presents some popular intelligent algorithms such as particle swarm optimization(PSO), genetic algorithm(GA), ant colony optimization (ACO), etc. When PV systems are affected by partial shading, these GMPPT (global maximum power point tracking) algorithm is required to increase the energy harvesting capability of the system. By analyze and discuss the search efficiency and convergence property of several intelligent optimization methods mentioned above, multiple problem still exist and the GMPP can not usually be tracked exactly and quickly. Thus, this article point out it is great significance that several hybrid intelligent algorithms are put forward to compensate the deficiency of single algorithms, and modified the optimization is also required.
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