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Artificial neural network technique for transmission line protection on Nigerian power system

机译:尼日利亚电力系统输电线路保护的人工神经网络技术

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摘要

This paper presents a unique and efficient artificial neural network (ANN) based fault detection, classification and location on part of the Nigerian 132kV transmission line. The objective is to evaluate the performance of ANN based relays connected at both ends of the lines using feed-forward non-linear supervised back propagation algorithm with Levenbergmarguardt network topology. Using the PSCAD/EMTP software, the faults from both ends of the transmission lines are generated and fed into that same line using two different 132kV voltage sources with several variations of fault inception angle, location and resistance. The faults currents are then extracted, processed and divided into training and testing data using MATLAB software. The results obtained from the simulations are validated using real-data extracted from microprocessor based relay connected to Aba-Umuahia 132kVtransmission line. The results demonstrate the ability of ANN to correctly identify, classify and localize an actual fault occurring on that transmission line with high accuracy.
机译:本文提出了一种独特而有效的人工神经网络(ANN),用于在尼日利亚132kV输电线路的一部分上进行故障检测,分类和定位。目的是使用具有Levenbergmarguardt网络拓扑的前馈非线性监督反向传播算法,评估连接在线路两端的基于ANN的继电器的性能。使用PSCAD / EMTP软件,可以使用两个不同的132kV电压源生成传输线两端的故障,并将其馈入同一条线路,故障起始角度,位置和电阻有多种变化。然后使用MATLAB软件提取,处理故障电流并将其分为训练和测试数据。使用从连接到Aba-Umuahia 132kV输电线路的基于微处理器的继电器提取的真实数据,可以验证从仿真中获得的结果。结果表明,人工神经网络能够正确地识别,分类和定位发生在该传输线上的实际故障,并具有较高的准确度。

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