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首页> 外文期刊>Journal of Electrical & Electronic Systems >Modeling of a Photovoltaic Array in MATLAB Simulink and Maximum Power Point Tracking Using Neural Network
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Modeling of a Photovoltaic Array in MATLAB Simulink and Maximum Power Point Tracking Using Neural Network

机译:MATLAB Simulink中的光伏阵列建模和使用神经网络的最大功率点跟踪

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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 P & O 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 P&O algorithm based MPPT.
机译:在本文中,我们介绍了使用神经网络进行最大功率点跟踪(MPPT)的工作。 MATLAB / Simulink用于建立光伏阵列的模型。 Simulink模型在不同温度和辐照下进行了测试,得到的I-V和P-V特性证明了PV阵列Simulink模型的有效性。在一定范围的辐照和温度下进行模拟之后,我们从PV阵列的Simulink模型收集了一组数据。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,我们看到该神经网络可以准确地预测光伏阵列的最大功率点。本文采用反向传播训练算法来训练神经网络。本文还显示了具有P&O算法和不具有MPPT跟踪器的MPPT的比较。结果表明,与基于P&O算法的MPPT相比,基于神经网络的MPPT跟踪所需的时间更少,并且结果更准确。

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