Field programmable gate array implementation of a fault location system in transmission lines based on artificial neural networks
Field programmable gate array implementation of a fault location system in transmission lines based on artificial neural networks
- Author(s): J. Ezquerra ; V. Valverde ; A.J. Mazón ; I. Zamora ; J.J. Zamora
- DOI: 10.1049/iet-gtd.2010.0273
For access to this article, please select a purchase option:
Buy article PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
Thank you
Your recommendation has been sent to your librarian.
- Author(s): J. Ezquerra 1 ; V. Valverde 2 ; A.J. Mazón 2 ; I. Zamora 2 ; J.J. Zamora 1
-
-
View affiliations
-
Affiliations:
1: Electronics and Telecommunications Department, Faculty of Engineering of Bilbao, University of the Basque Country, Bilbao, Spain
2: Electrical Engineering Department, Faculty of Engineering of Bilbao, University of the Basque Country, Bilbao, Spain
-
Affiliations:
1: Electronics and Telecommunications Department, Faculty of Engineering of Bilbao, University of the Basque Country, Bilbao, Spain
- Source:
Volume 5, Issue 2,
February 2011,
p.
191 – 198
DOI: 10.1049/iet-gtd.2010.0273 , Print ISSN 1751-8687, Online ISSN 1751-8695
In the field of electrical engineering there are many areas where artificial neural networks (ANNs) are being applied. ANNs can be modelled and implemented by means of the execution of specific software in sequential computers. Nevertheless, if a real-time application wants to be developed, it is necessary to study how to use its massively parallel processing capacity by a specific electronic system. This study presents the methodology, the design and the field programmable gate array implementation process of a fault classification and location prototype for overhead transmission lines, based on ANNs.
Inspec keywords: power transmission faults; fault location; field programmable gate arrays; power engineering computing; power overhead lines; neural nets
Other keywords:
Subjects: Neural computing techniques; Logic and switching circuits; Overhead power lines; Power engineering computing; Logic circuits
References
-
-
1)
- Xilinx, Inc: Spartan-3 FPGA Family Datasheet, 2009, http://www.xilinx.com.
-
2)
- Vasilic, S., Kezunovic, M.: `An improved neural network algorithm for classifying the transmission line faults', IEEE PES Winter Meeting, January 2002.
-
3)
- Avnet Electronics Marketing: Xilinx Spartan-3 Evaluation Kit – Brief 022504F (pdf), Design Resource Center, http://www.avnet.com.
-
4)
- A.J. Mazon , I. Zamora , J.F. Miñambres , M.A. Zorrozua , J.J. Barandiaran , K. Sagastabeitia . A new approach to fault location in two-terminal transmission lines using artificial neural networks. Electric Power Syst. Res. J. , 261 - 266
-
5)
- A.J. Mazon , I. Zamora , K.J. Sagastabeitia , V. Valverde . Strategies for fault classification in transmission lines, using learning vector quantization neural networks. Eur. Trans. Electr. Power Eng. , 4 , 365 - 378
-
6)
- S.A. Khaparde , N. Warke , S.H. Agarwal . An adaptive approach in distance protection using an artificial neural network. Electric Power Syst. Res. , 1 , 39 - 44
-
7)
- F. Zahra , B. Jeyasurya , J.E. Quaicoe . High-speed transmission line relaying using artificial neural networks. Electr. Power Syst. Res. , 173 - 179
-
8)
- Xilinx Inc: ‘Embedded system tools reference manual’ (Xilinx, UGi 1 1(v6.0), June 23, 2006), http://www.xilinx.com.
-
9)
- Eldredge, J., Hutchings, B.: `Density enhancement of a neural network using fpgas and run-time reconfiguration', IEEE Symp. on FPGAs for Custom Computing Machines (FCCM94), 1994, p. 180–188.
-
10)
- Z. Chen , J.-C. Maun . Artificial neural network approach to single-ended fault locator for transmission lines. IEEE Trans. Power Syst. , 1 , 370 - 375
-
11)
- W.M. Lin , C.H. Yang , J.H. Lin , M.T. Tsay . A fault classification method by RBF neural network with OLS learning procedure. IEEE Trans. Power Deliv. , 4 , 473 - 477
-
12)
- Bharkhada, B., Hauser, J., Purdy, C.: `Efficient FPGA implementation of a generic function approximator and its application to neural net computation', Proc. IEEE Midwest Symp. on Circuits and Systems (MWSCAS’03), December 2003, Cairo, Egypt.
-
13)
- Mazon, A.J., Zamora, I., Gracia, J., Bilbao, J., Saenz, J.R.: `Falneur: artificial neural network based software to fault location in electrical transmission lines', IASTED Int. Conf. Applied Informatics (AI2001), February 2001, Innsbruck, Austria.
-
14)
- A.K. Pradhan , P.K. Dash , G. Panda . A fast and accurate distance relaying scheme using an efficient radial basis function neural network. Electr. Power Syst. Res. , 1 - 8
-
15)
- Zhu, J., Sutton, P.: `FPGA implementations of neural networks – a survey of a decade of progress', Proc. Int. Conf. Field-Programmable Logic and Applications (FPL 2003), September 2003, Lisbon, Portugal, p. 1062–1066.
-
16)
- R.K. Aggarwal , Q.Y. Xuan , R.W. Dunn . A novel fault classification technique for double circuit lines based on a combined unsupervised/supervised neural network. IEEE Trans. Power Deliv. , 4 , 1250 - 1256
-
17)
- K. Gurney . (1997) An introduction to neural networks.
-
18)
- A.J. Mazon , I. Zamora , J. Gracia , K.J. Sagastabeitia , J.R. Saenz . Selecting ANN structures to find transmission faults. IEEE Comput. Appl. Power , 3 , 44 - 48
-
19)
- J. Gracia , A.J. Mazon , I. Zamora . Best ANN structures for fault location in single and double circuit transmission lines. IEEE Trans. Power Deliv. , 4 , 2389 - 2395
-
20)
- Xilinx Inc: ‘MicroBlaze processor reference guide’ (Xilinx, UG081 (v6.0), June 1, 2006), http://www.xilinx.com.
-
21)
- M. Chester . (1993) Neural networks tutorial.
-
1)
Related content
