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Fault Location in the Transmission Network Using Artificial Neural Network

机译：使用人工神经网络传输网络中的故障位置

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摘要

In this paper, in order to locate the fault in the transmission network, a discrete wavelet transform is used to extract the fault characteristics from the zero-sequence current, in order to train the artificial neural network. In fact, the basis of the work is based on the information recorded after the fault at the beginning and at the end of the line, received by the relay. In the following, with the help of Fortescue's transform, the current of zero sequence seen from both terminals is calculated and by the transform of the wavelet of stored information at high frequency is extracted in the horizontal components of the zero sequence current from both terminals, and finally calculating the energy stored in horizontal components, as well as extracting the maximum scales of horizontal components can reveal certain features of the fault that are suitable for training the neural network. Simulation results show that the maximum scales of horizontal components and the energy stored in these components strongly depend on the fault resistance, type of fault, fault angle and fault location. Therefore, the training data should be selected in such a way that these changes are well represented so that the neural network does not encounter problem in its diagnosis. Finally, the proposed method has been tested on a transmission network of 735 kV at different distances of the transmission line. And results indicate that the proposed algorithm can estimate fault distance depending on the type of fault in different conditions.
机译：在本文中，为了定位传输网络中的故障，使用离散小波变换来从零序电流中提取故障特性，以训练人工神经网络。实际上，工作的基础是基于在线接收到的开头和末尾的故障后记录的信息。在下文中，在Fortescue的变换的帮助下，计算从两个端子的零序列的电流计算，并且通过在来自两个终端的零序电流的水平分量的高频上提取高频的存储信息的小波的转换，最后计算存储在水平组件中的能量，以及提取水平分量的最大尺度可以揭示适合训练神经网络的故障的某些特征。仿真结果表明，水平部件的最大尺度和存储在这些部件中的能量强烈取决于故障电阻，故障类型，故障角度和故障位置。因此，应以这种方式选择培训数据，使得这些变化很好地表示，使得神经网络在其诊断中没有遇到问题。最后，在传输线的不同距离下已经在735kV的传输网络上测试了所提出的方法。结果表明，所提出的算法可以根据不同条件的故障类型估计故障距离。

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