Power System Fault Detection and Classification Using Deep Neural Network


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Power System Fault Detection and Classification Using Deep Neural Network ================================================================

This video focuses on the detection and classification of faults on electrical power distribution lines using artificial neural networks. The three-phase currents and voltages of feeders are taken as inputs in the proposed scheme. The feed-forward neural network along with the backpropagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying numbers of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the transmission network of the Power System. The various simulations and analyses of signals are done in the MATLAB® environment.

Fault detection Accuracy : 100 %; Fault Classification Accuracy : 90.1 % ===============================================================

You can download a MATLAB file from the following Link: Click



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