Maximum Power Point Tracking (MPPT) for Photovoltaic systems using open circuit voltage and short circuit current

S. Hadji, J. Gaubert, F. Krim
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引用次数: 20

Abstract

This paper deals with a new Maximum Power Point Tracking (MPPT) method for Photovoltaic (PV) systems based on Genetic Algorithms (GAs). The proposed algorithm can estimate the current (Impp) and voltage (Vmpp) at maximum power point by measuring the open circuit voltage (Voc) and the short circuit current (Isc) without knowing the irradiance and the cell temperature. To study this method, Matlab/Simulink is used to implement both the algorithm and PV array model. We also give a comparison with the conventional Perturb and Observe (P&O) and Incremental Conductance (Inc-Cond) methods, we observe the advantages about: - Oscillations around the maximum power point. - Response to a rapid atmospheric changing. In GAs we search for a maximum of fitness function (at MPP) while with P&O and Inc-Cond we search for minimal value power derivation, so we have better stability with AGs method.
利用开路电压和短路电流的光伏系统的最大功率点跟踪(MPPT)
提出了一种基于遗传算法的光伏系统最大功率点跟踪方法。该算法可以在不知道辐照度和电池温度的情况下,通过测量开路电压(Voc)和短路电流(Isc)来估计最大功率点的电流(Impp)和电压(Vmpp)。为了研究该方法,使用Matlab/Simulink实现了算法和光伏阵列模型。我们还与传统的扰动和观察(P&O)和增量电导(Inc-Cond)方法进行了比较,我们观察到最大功率点周围振荡的优点。-对大气快速变化的反应。在GAs方法中,我们寻找适应度函数(在MPP处)的最大值,而在P&O和incc - cond方法中,我们寻找最小值幂导数,因此AGs方法具有更好的稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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