ANN based fault location for medium voltage distribution lines with remote-end source

Y. Aslan, Y. E. Yağan
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引用次数: 5

Abstract

This study presents a fault location algorithm for medium voltage (MV) overhead power distribution lines based on artificial neural network (ANN). In the study the possibility of connection of a small scale remote-end source connection to the end of a radial distribution feeder has been considered. In the study, feed forward ANN with back propagation algorithm with Levenberg-Marquardt training function is used. The ANN inputs were formed by using frequency information of fault data which were obtained with digital filtering. The algorithm is extensively tested for a various system conditions for the faults created on the overhead distribution system which has been modeled with Matlab/Simulink software. From the results attained it is seen that the proposed technique has not been significantly affected from the connection of a small scale hydroelectric generator to the existing distribution system.
基于神经网络的远端电源中压配电线路故障定位
提出了一种基于人工神经网络的中压架空配电线路故障定位算法。在研究中,考虑了将小型远端源连接到径向配料器末端的可能性。本研究采用带Levenberg-Marquardt训练函数的前馈神经网络反向传播算法。通过数字滤波获得故障数据的频率信息,形成人工神经网络输入。针对架空配电系统产生的故障,采用Matlab/Simulink软件对该算法进行了广泛的系统工况测试。结果表明,小型水轮发电机与现有配电系统的连接对所提出的技术没有明显影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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