Ann Based Fault Detection & Classification of A 400 Kv Electrical Transmission Line

!!!! Bi-Annual Double Blind Peer Reviewed Refereed Journal !!!!

!!!! Open Access Journal !!!!


Gaurav Gangil - M.E. student, Electrical Engineering Department, MITS Gwalior

Prof. Rakesh Narvey - Assistant Professor Electrical Engineering Department, MITS Gwalior


The protection of transmission line have a great challenging task. A novel approach is used for protection of transmission line is presented in this paper. This paper focuses on fault detection and then classifying the fault. This fault Detection and fault classification is based on artificial neural network. In ANN we used feed-forward back-propagation neural network are designed to detect and classify the fault on a 400 KV, 200 Km transmission line. The features of neural networks, such as their ability to learn, generalize and parallel processing, among others, have made their applications for many systems ideal. The use of neural networks as pattern classifiers is among their most common and powerful applications. The proposed algorithm used the voltage and current signal measured at on end to detect and classify the fault. An improved performance is experienced once the neural network is trained give accurate result. The entire results show that the fault is detected and their classification. Result of performance studies shows that the proposed neural network gives more accurate results.

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