A backpropagation feedforward NN for fault detection and classifying of overhead bipolar HVDC TL using DC measurements
Joint Authors
Source
Journal of Engineering Research and Technology
Issue
Vol. 2, Issue 3 (30 Sep. 2015), pp.197-202, 6 p.
Publisher
The Islamic University-Gaza Deanship of Research and Graduate Affairs
Publication Date
2015-09-30
Country of Publication
Palestine (Gaza Strip)
No. of Pages
6
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
This paper suggests the use of back-propagation feed-forward artificial neural networks (NN) for fault detection and classification in the high voltage direct current (HVDC) transmission line (TL).
To achieve these tasks, post-fault measurements of the dc voltages and currents at the rectifier station related to the pre-fault measurements are used as inputs to the neural network.
A bipolar HVDC TL model of 940 km long and ±500 kV is chosen to be studied.
This paper handles most frequent kinds of overhead bipolar HVDC TL power faults, and the results obtained are completely satisfactory.
American Psychological Association (APA)
Abu Jasir, Asad& Ashur, Mahmud. 2015. A backpropagation feedforward NN for fault detection and classifying of overhead bipolar HVDC TL using DC measurements. Journal of Engineering Research and Technology،Vol. 2, no. 3, pp.197-202.
https://search.emarefa.net/detail/BIM-720846
Modern Language Association (MLA)
Abu Jasir, Asad& Ashur, Mahmud. A backpropagation feedforward NN for fault detection and classifying of overhead bipolar HVDC TL using DC measurements. Journal of Engineering Research and Technology Vol. 2, no. 3 (Sep. 2015), pp.197-202.
https://search.emarefa.net/detail/BIM-720846
American Medical Association (AMA)
Abu Jasir, Asad& Ashur, Mahmud. A backpropagation feedforward NN for fault detection and classifying of overhead bipolar HVDC TL using DC measurements. Journal of Engineering Research and Technology. 2015. Vol. 2, no. 3, pp.197-202.
https://search.emarefa.net/detail/BIM-720846
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 202
Record ID
BIM-720846