Abstract

The maximum power point tracking (MPPT) controller holds an important role in increasing the efficiency of the photovoltaic (PV) system. However, conventional MPPT techniques may fail to locate the global maximum power point (GMPP) under partial shading conditions (PSC). Hence, to optimize the efficiency of PV systems, we introduce a new MPPT technique for PSC. The proposed method employs an artificial neural network (ANN) to predict the area of the GMPP, and the classic perturb and observe (P&O) algorithm to locate the exact position of the GMPP. We validated the effectiveness of the technique using computer simulations performed with the MATLAB/Simulink program, the results of which verified that it can track the GMPP faster than other methods.

中文翻译：

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部分遮蔽条件下基于神经网络的光伏系统快速最大功率点跟踪方法

摘要

最大功率点跟踪（MPPT）控制器在提高光伏（PV）系统的效率方面起着重要作用。但是，传统的MPPT技术可能无法在部分阴影条件（PSC）下定位全局最大功率点（GMPP）。因此，为了优化光伏系统的效率，我们为PSC引入了一种新的MPPT技术。该方法采用人工神经网络（ANN）预测GMPP的面积，并采用经典的扰动观察（P＆O）算法来定位GMPP的确切位置。我们使用在MATLAB / Simulink程序上进行的计算机仿真验证了该技术的有效性，其结果验证了该技术比其他方法可以更快地跟踪GMPP。