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Probabilistic Wavelet Fuzzy Neural Network based reactive power control for grid-connected three-phase PV system during grid faults

机译:电网故障时基于概率小波模糊神经网络的并网三相光伏系统无功控制

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This study presents a reactive power controller using Probabilistic Wavelet Fuzzy Neural Network (PWFNN) for grid-connected three-phase PhotoVoltaic (PV) system during grid faults. The controller also considers the ratio of the injected reactive current to meet the Low Voltage Ride Through (LVRT) regulation. Moreover, the balance of the active power between the PV panel and the grid-connected inverter during grid faults is controlled by the dc-link bus voltage. Furthermore, to reduce the risk of over-current during LVRT operation, a current limit is predefined for the injection of reactive current. The main contribution of this study is the introduction of the PWFNN controller for reactive and active power control that provides LVRT operation with power balance under various grid fault conditions. Finally, some experimental tests are realized to validate the effectiveness of the proposed controller. (C) 2016 Published by Elsevier Ltd.
机译:这项研究提出了一种使用概率小波模糊神经网络(PWFNN)的无功功率控制器,用于电网故障期间并网的三相光伏(PV)系统。控制器还考虑注入的无功电流的比率,以满足低电压穿越(LVRT)规定。此外,在电网故障期间,光伏面板和并网逆变器之间的有功功率平衡由直流母线电压控制。此外,为了降低LVRT操作期间过电流的风险,为注入无功电流预定义了电流限制。这项研究的主要贡献是引入了用于无功和有功功率控制的PWFNN控制器,该控制器可在各种​​电网故障条件下为LVRT运行提供功率平衡。最后,通过一些实验测试来验证所提出控制器的有效性。 (C)2016由Elsevier Ltd.出版

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