部分遮阳光伏系统最大功率点跟踪的加速粒子群算法

R. Subha, S. Himavathi
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引用次数: 9

摘要

近年来,由于向可再生能源的范式转变,光伏(PV)系统受到了广泛关注。PV系统的P-V曲线有一个独特的点,在这个点上功率最大,这个点随着辐照度和电池温度的变化而变化。因此,最大功率点跟踪(MPPT)算法必须到位,以确保提取最大功率。在大型光伏阵列中,一个或多个电池可能由于附近的建筑物或经过的云而被遮蔽。这种部分阴影阵列的P-V曲线有多个峰值,算法必须识别全局峰值。传统的MPPT算法无法做到这一点。近年来,生物启发粒子群优化算法(PSO)已经显示出其在任何遮阳条件下都能从阵列中提取最大功率的能力。本文采用加速粒子群算法(APSO),这是粒子群算法的简化版。通过对不同遮光模式的仿真,验证了该算法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerated particle swarm optimization algorithm for maximum power point tracking in partially shaded PV systems
Photovoltaic (PV) systems have gained major attention in the recent past due to the paradigm shift to renewable energy sources. The P-V curve of a PV system has a unique point at which power is maximum and this point varies with changing irradiance and cell temperature. Hence a maximum power point tracking (MPPT) algorithm has to be in place to make sure that maximum power is extracted. In large PV arrays one or more cells may get shaded due to a nearby building or a passing cloud. The P-V curve of such a partially shaded array has more than one peak and the algorithm has to identify the global peak. Conventional MPPT algorithms fail to do this. Recently the bio inspired particle swarm optimization (PSO) algorithm has shown its ability extract maximum power from an array under any shading condition. This paper uses accelerated PSO (APSO) algorithm, a simplified version of the PSO algorithm. The performance of this algorithm has been validated through simulation for different shading patterns.
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