Artificial neural networks and wavelet transform have been used to achieve fault Identification and classification on electric power transmission lines.This work proposed an improved solution based on wavelet transform and neural network back-propagation algorithm. The three-phase current and voltage waveforms measured during the occurrence of fault in the power transmission-line are pre-processed first and then decomposed using wavelet multi-resolution analysis to obtain the high frequency details and low frequency approximations. The patterns formed based on high frequency signal components are arranged as inputs of the neural network, whose task is to indicate the occurrence of a fault on the lines. The patterns formed using low frequency approximations are arranged as inputs of the second neural network, whose task is to indicate the exact fault type. The neural networks which can learn was trained to recognize patterns, classify data and forecast future events. Feed forward networks have been employed along with back propagation algorithm for each of the three phases in the Fault location process. An analysis of the learning and generalization characteristics of elements in power system was presented using Neural Network toolbox in MATLAB/SIMULINK environment. Simulation results obtained demonstrated that neural network pattern recognition and wavelet multi-resolution analysis approachare efficient in identifying and classifying faults on transmission lines as satisfactory performance was achieved especially when compared to the conventional methods such as impedance and travelling wave methods.
Pattern recognition, Feed forward back propagation algorithm, Neural network, Levenberg-Marquardt algorithm, Power system protection
Ezechukwu O. A. , Aneke Jude I. , Ebune R. U. "Artificial Intelligence Based Fault Detection And Classification In Transmission Lines" Iconic Research And Engineering Journals Volume 2 Issue 4 2018 Page 38-46
Ezechukwu O. A. , Aneke Jude I. , Ebune R. U. "Artificial Intelligence Based Fault Detection And Classification In Transmission Lines" Iconic Research And Engineering Journals, 2(4)