This article proposes an Artificial Neural Network (ANN) controller of Dynamic Voltage Restorer (DVR) to improve the performance of a stand-alone hybrid renewable energy system that is feeding a new community located in Egypt. The hybrid system consists of three renewable energy sources, namely, solar PV cells, a wind turbines based-permanent magnet synchronous generator, and fuel cells. These three sources are tied to a common DC link by three boost converters, one for each source. The common DC link is connected to the AC side via a DC/AC inverter. The optimal size of the three proposed renewable sources is calculated using the HOMER software package. The DVR control is attained through regulating the load voltage at different anomalous working conditions. These conditions are three-phase fault, voltage sag/swell, and unbalanced loading. Two ANNs are utilized to adjust the IGBT pulses of the voltage source inverter (VSI) used to control DVR by regulating the D-Q axes voltage signals. These D-Q axes components at any loading condition represent the inputs to the two ANNs. The outputs of the two ANNs represent the IGBT pulses. The input/output data used for training ANNs are obtained by two optimized PI controllers, introduced for regulating the load voltage through DVR-VSI pulses at different abnormal operating conditions, and accordingly convert the static optimized PI controller into adaptive one based ANN. The system performance with the proposed ANN-DVR controller is enhanced through improving the current, voltage, and power waveforms of each generating source. With compensation of the faulty line voltage, the system retains an uninterrupted operation of the three renewable sources during fault events and consequently increases the low voltage ride through (LVRT) capability. Moreover, the total harmonic distortion is reduced.

本文提出了一种动态电压恢复器（DVR）的人工神经网络（ANN）控制器，以改善为埃及一个新社区提供食物的独立混合可再生能源系统的性能。混合动力系统由三种可再生能源组成，即太阳能光伏电池，基于风力涡轮机的永磁同步发电机和燃料电池。这三个电源通过三个升压转换器与一个公共直流链路相连，每个升压转换器一个。公用DC链路通过DC / AC逆变器连接到AC侧。使用HOMER软件包计算了三种建议的可再生能源的最佳尺寸。DVR控制是通过在不同的异常工作条件下调节负载电压来实现的。这些条件是三相故障，电压骤降/骤升和不平衡负载。通过调节DQ轴电压信号，利用两个ANN来调节用于控制DVR的电压源逆变器（VSI）的IGBT脉冲。这些DQ轴组件在任何负载条件下均代表两个ANN的输入。两个ANN的输出代表IGBT脉冲。用于训练ANN的输入/输出数据是通过两个优化的PI控制器获得的，引入该控制器可通过DVR-VSI脉冲在不同的异常操作条件下调节负载电压，并将静态优化的PI控制器转换为自适应的基于ANN的控制器。所提出的ANN-DVR控制器的系统性能通过改善每个发电源的电流，电压和功率波形而得到增强。通过补偿故障线路电压，在发生故障事件时，该系统可保持三个可再生能源的不间断运行，从而提高了低压穿越（LVRT）能力。此外，总谐波失真得以减小。