Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN

Purva Sharma, Deepak Saini, Akash Saxena


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.


Wavelet transform, Daubechies wavelet, artificial neural network, Supervised leaning method, Mean square error, Mean absolute error, Root mean square error, Sum square error

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DOI: https://doi.org/10.11591/eei.v5i3.537


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Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
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