Fault Location in the Transmission Network Using Artificial Neural Network

Abstract

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.