Application of artificial neural network (ANN) to enhance power systems protection: a case of the Nigerian 330 kV transmission line

This work investigates an improved protection solution based on the use of artificial neural network on the 330 kV Nigerian network modeled using MATLAB R2014a. Measured fault voltages and currents signals decomposed using the discrete Fourier transform implemented via fast Fourier transform are fed as inputs to the neural network. The output plots of the neural network shows its successful application to fault diagnosis (fault detection, fault classification and fault location). Unlike conventional protection schemes, the neural network can be adapted to distances which can cover the entire length of the protected line. Numerical assessment carried out on the neural network fault locator shows a reduced time of operation of 5.15 ms as compared to the 0.350 s with the use of ordinary numerical relays. This work also investigates the adaptive auto-reclosure scheme implemented using artificial neural network. The adaptive reclosure scheme has been adapted for use in the Nigerian network successfully to distinguish transient and permanent faults. Simulation results prove that the adaptive reclosure scheme was able to detect a line-to-ground transient fault and clear this fault in 0.1 s while the line-to-ground permanent fault is cleared after 0.14 s. The fault diagnostic algorithm designed using artificial neural network (ANN) for the 330 kV network was tested on a 132 kV network. Results show and prove that the algorithm is flexible and can be adopted to other networks.