Open AccessFault Detection and Classification in Transmission Line Using Wavelet Transform and ANN
Author(s) -
Purva Sharma,
Deepak Saini,
Akash Saxena
Publication year - 2016
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v5i3.537
Subject(s) - mean squared error , wavelet transform , wavelet , artificial neural network , fault (geology) , pattern recognition (psychology) , artificial intelligence , computer science , matlab , discrete wavelet transform , algorithm , machine learning , mathematics , statistics , seismology , geology , operating system
Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed that ANN architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network.