Research Article
Manish Khandelwal · Amit Solanki
Journal
International Journal of Digital Applications and Contemporary Research (IJDACR)
ISSN
2319-4863
Volume / Issue
Vol.5 · Issue 4
Published
November 2016
Access
Open Access
Licence
CC BY-NC-SA 4.0
Electrical power systems suffer from unexpected failures due to various random causes. The functions of the protective systems are to detect, then classify and finally determine the location of the faulty line of voltage and/or current line magnitudes. Then at last, for isolation of the faulty line the protective relay have to send a signal to the circuit breaker. The ability to learn, generalize and parallel processing, pattern classifiers is applications of Neural Network used as an intelligent tool for detection.
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 use of back-propagation neural network architecture as an alternative method for fault detection, classification and isolation in a transmission line system. The main goal is the implementation of complete scheme for distance protection of a transmission line system. In order to perform this, the distance protection task is subdivided into different neural networks for fault detection, fault identification (classification) as well as fault location in different zones. Three common faults were discussed; single phase to ground faults, double phase faults and double phase to ground faults. The result provides a reliable and an attractive alternative approach for the development of a protection relaying system for the power transmission systems.
Manish Khandelwal, Amit Solanki (2016). Transmission Line Fault Detection and Classification using Artificial Neural Network. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.5, Issue 4. ISSN: 2319-4863.
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