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Neural network based maximum power point tracking scheme for PV systems operating under partially shaded conditions

机译:在部分阴影条件下运行的光伏系统的基于神经网络的最大功率点跟踪方案

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摘要

Photovoltaic (PV) Systems have gained significant attention due to its advantages like abundant availability, eco-friendly nature and low maintenance requirement. The P-V characteristic of the solar panel has a unique Maximum Power Point (MPP). To ensure that maximum power is extracted from the panels Maximum Power Point Tracking (MPPT) algorithm is utilized. In order to meet the voltage and current requirements large number of panels are connected in series-parallel combinations. The performance of such large arrays is adversely affected due to partial shading. This is due to the multiple peaks in the P-V Characteristics of the array under partial shading. Conventional MPPT algorithms have failed to detect the global peak under such conditions. Hence a Neural Network (NN) based MPPT algorithm has been proposed in this paper. The proposed algorithm has been verified by simulation for various partially shaded conditions and shown to perform well.
机译:光伏(PV)系统由于其优势如可用性,生态友好性和低维护要求而受到了广泛的关注。太阳能电池板的P-V特性具有唯一的最大功率点(MPP)。为了确保从面板中提取最大功率,使用了最大功率点跟踪(MPPT)算法。为了满足电压和电流要求,将大量面板串联-并联组合连接。这样的大阵列的性能由于部分阴影而受到不利影响。这是由于部分阴影下阵列的P-V特性中存在多个峰值。传统的MPPT算法在这种情况下无法检测到全局峰值。因此,本文提出了一种基于神经网络的MPPT算法。所提出的算法已通过仿真验证了各种阴影部分条件,并显示出良好的性能。

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