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Modeling of a photovoltaic array in MATLAB simulink and maximum power point tracking using neural network

机译:使用神经网络模拟Matlab Simulink和最大功率点跟踪的光伏阵列

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In this paper, we present our work on Maximum Power Point Tracking (MPPT) using neural network. The MATLAB/Simulink is used to establish a model of photovoltaic array. The Simulink model is tested with different temperature and irradiation and resultant I-V and P-V characteristics proved the validation of Simulink model of PV array. We collected a set of data from the Simulink model of PV array after simulated under a range of irradiation and temperature. The data collected from the system is used to train the neural network. When we tested the neural network with different irradiance and temperature, we see that the neural network can accurately predict the maximum power point of a photovoltaic array. In this paper, the backpropagation training algorithm is used to train the neural network. Comparisons of MPPT with PandO algorithm and without MPPT tracker are also shown in this paper. It is demonstrated that the neural network based MPPT tracking require less time and provide more accurate results than the algorithm based MPPT.
机译:在本文中,我们使用神经网络介绍了我们对最大功率点跟踪(MPPT)的工作。 MATLAB / SIMULINK用于建立光伏阵列的模型。使用不同的温度和照射和结果I-V和P-V特征来测试Simulink模型证明了PV阵列的Simulink模型的验证。在辐照和温度范围内模拟后,我们从PV阵列的Simulink模型中收集了一组数据。从系统收集的数据用于训练神经网络。当我们用不同的辐照度和温度测试神经网络时,我们看到神经网络可以准确地预测光伏阵列的最大功率点。在本文中,使用BackProjagation训练算法训练神经网络。本文还示出了具有PANDO算法和没有MPPT跟踪器的MPPT的比较。据证明基于神经网络的MPPT跟踪需要更少的时间并提供比基于算法的MPPT更精确的结果。

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