NDLI: Detection and classification of faults on six phase transmission line using ANN

Content Provider	IEEE Xplore Digital Library
Author	Koley, E. Jain, A. Thoke, A.S. Ghosh, S.
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Electronics Engineering, Birla Institute Of Technology, Mesra, Jharkhand India (Ghosh, S.) \|\| Department of Electrical Engineering, N.I.T. Raipur, C.G. India (Koley, E.; Jain, A.; Thoke, A.S.; Jain, A.)
Abstract	The demand of electrical energy is continuously increasing. High phase order transmission system is a viable alternative due to increasing costs of right of way. Six phase transmission lines can carry more power for same phase to phase voltage with the same right of way economically. Protection of six phase transmission lines has been a very challenging task. Earlier simulations of six phase transmission line have been done in PSCAD/EMTDC software. In this paper MATLAB® software and its associated Simulink® and Simpowersystem® toolboxes have been used to simulate the six phase transmission line. Application of Artificial Neural Network for protection of six phase transmission line against ground faults is presented, which the authors believe has not been reported earlier. Fundamental components of six phase voltages and currents have been used as inputs for training of the artificial neural network for detection and classification of faulted phase using neural network toolbox of MATLAB®. A sample 765 kV system of 60 km length has been selected for study. The study takes into account the effect of variation in fault inception angle, fault distance location and fault resistance. The results indicate the suitability of proposed technique and its adaptability to changing system conditions.
Starting Page	100
Ending Page	103
File Size	643546
Page Count	4
File Format	PDF
ISBN	9781457713859
e-ISBN	9781457713866
DOI	10.1109/ICCCT.2011.6075177
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-15
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Resistance Power transmission lines Fault detection Artificial Neural Network Neurons Artificial neural networks Fault detection and classification High phase order transmission system Circuit faults
Content Type	Text
Resource Type	Article

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