This paper presents a discrete wavelet transform and neural network approach to fault detection and classification in transmission line faults. The detection is carried out by the analysis of the details coefficients energy of the phase signals, and as an input to neural network to classify the faults on transmission lines. Neural network perform well when faced with different fault conditions and system parameters. In this paper, WT has been applied to the output phase A unbalance fault voltage and current signals of a typical transmission line modeled with MATLAB/SIMULINK 2016. The Phase A were simulated on the line and their pre ? fault and fault voltage and current per ? unit output values were generated and produced waveforms of pre ? fault and fault signals. The results of the MRA fault detection analysis show that the wavelet transform method is more accurate in detecting the various faults of a transmission line than any other signal analysis techniques.
Wavelet Transform, Fault detection, Transmission line, Multi resolution analysis, Transient energy
V. C. OGBOH , C. P. EZEAKUDO , E. C. NWANGUGU "Wavelet Transform Technique For Fault Detection On Power System Transmission Line" Iconic Research And Engineering Journals Volume 3 Issue 3 2019 Page 47-52
V. C. OGBOH , C. P. EZEAKUDO , E. C. NWANGUGU "Wavelet Transform Technique For Fault Detection On Power System Transmission Line" Iconic Research And Engineering Journals, 3(3)