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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人工神经网络技术

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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：本文介绍了基于独特高效的人工神经网络（ANN）基于尼日利亚132kV传输线的一部分的故障检测，分类和位置。目的是评估在线的两端连接基于AN的继电器的性能，使用前馈非线性监控的反向传播算法与LevenbergMarguardt网络拓扑结构进行了馈线。使用PSCAD / EMTP软件，使用两个不同的132kV电压源产生传输线两端的故障，并使用两个不同的132kV电压源进入该相同线，具有几个故障成立角，位置和电阻。然后，使用MATLAB软件提取故障电流，处理和分为训练和测试数据。使用从基于微处理器的继电器提取的实际数据验证了从模拟获得的结果，连接到ABA-Umuahia132kvtransmission线。结果证明了ANN以正确识别，分类和本地化在该传输线上的实际断层的能力，精度高。

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