Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System
Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System
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DOI：
10.3390/en12142827
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发表时间：
2019-07-02
期刊：
影响因子：
3.2
通讯作者：
Batoun, Bachir
中科院分区：
文献类型：
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作者：
Bouarroudj, Noureddine;Boukhetala, Djamel;Batoun, Bachir
提出了一种新型的光伏系统最大功率点跟踪（MPPT）控制器。建议的MPPT控制器的设计，以提取最大的功率从光伏模块和减少振荡，一旦最大功率点（MPP）已经实现。为了达到这个目标，模糊逻辑和自适应径向基函数神经网络（RBF-NN）的组合被用来驱动DC-DC升压转换器，用于连接光伏模块和电阻性负载。首先，一个模糊逻辑系统，其单输入是基于增量电导（INC）的方法，用于可变电压步长搜索，同时减少周围的MPP振荡。其次，RBF-NN控制器的开发，以保持光伏组件的电压在从第一阶段产生的最佳电压。为了确保在所有情况下（天气条件变化和负载变化）的真实的MPPT，使用基于梯度下降法的反向传播算法的自适应律来调整RBF-NN的权重，以最小化均方误差（MSE）标准。最后，通过仿真结果，我们提出的MPPT方法优于经典的P和O和INC自适应RBF-NN的效率。
In this article, a novel maximum power point tracking (MPPT) controller for a photovoltaic (PV) system is presented. The proposed MPPT controller was designed in order to extract the maximum of power from the PV-module and reduce the oscillations once the maximum power point (MPP) had been achieved. To reach this goal, a combination of fuzzy logic and an adaptive radial basis function neural network (RBF-NN) was used to drive a DC-DC Boost converter which was used to link the PV-module and a resistive load. First, a fuzzy logic system, whose single input was based on the incremental conductance (INC) method, was used for a variable voltage step size searching while reducing the oscillations around the MPP. Second, an RBF-NN controller was developed to keep the PV-module voltage at the optimal voltage generated from the first stage. To ensure a real MPPT in all cases (change of weather conditions and load variation) an adaptive law based on backpropagation algorithm with the gradient descent method was used to tune the weights of RBF-NN in order to minimize a mean-squared-error (MSE) criterion. Finally, through the simulation results, our proposed MPPT method outperforms the classical P and O and INC-adaptive RBF-NN in terms of efficiency.