NEURAL NETWORKS APPLICATION TO FAULT DETECTION IN ELECTRICAL SUBSTATIONS

Luiz Biondi Neto, Pedro Henrique Gouvêa Coelho, Alexandre Mendonça Lopes, Marcelo Nestor da Silva, David Targueta

Abstract

This paper proposes an application of neural networks to fault detection in electrical substations, particularly to the Parada Angélica Electrical Substation, part of the AMPLA Energy System provider in Rio de Janeiro, Brazil. For research purposes, that substation was modeled in a bay oriented fashion instead of component oriented. Moreover, the modeling process assumed a substation division in five sectors or set of bays comprising components and protection equipments. These five sectors are: 11 feed bays, 2 capacitor bank bays, 2 general/secundary bays, 2 line bays and 2 backward bays. Electrical power engineer experts mapped 291 faults into 134 alarms. The employed neural networks, also bay oriented, were trained using the Levenberg-Marquardt method, and the AMPLA experts validated training patterns, for each bay. The test patterns were directly obtained from the SCADA (Supervisory Control And Data Acquisition) digital system signal, suitably decoded were supplied by AMPLA engineers. The resulting maximum percentage error obtained by the fault detection neural networks was within 1.5 % which indicates the success of the used neural networks to the fault detection problem. It should be stressed that the human experts should be the only ones responsible for the decision task and for returning the substation safely into normal operation after a fault occurrence. The role of the neural networks fault detectors are to support the decision making task done by the experts.

References

  1. Kwang-Ho Kim and Jong-Keun Park, 1993. Application of hierarchical neural networks to faults diagnosis of power systems, Electrical Power & Energy Systems, Vol 15 No. 2 , pp 65-70.
  2. Biondi Neto, L. ; Ferreira, R. A. B., Targueta, David ; Mello, João Carlos Correia Baptista Soares, 2007. Intelligent System for Detection Fault in Electrical Systems, III European-Latin-American Workshop on Engineering Systems, 2007, Curicó, 2007.
  3. Cascaes Pereira, A.; Cáceres, David; Biondi Neto, L.; Ordacgi Filho, J. M.; Correia, J. R.; Pellizzoni, R., 2007. Rede de IEDs de Proteção como obter o máximo benefício para proteção e automação de Subestações, XII Encontro Regional Ibero-americano do CIGRÉ - ERIAC, 2007, Foz do Iguaçu. XII ERIAC.
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Paper Citation


in Harvard Style

Biondi Neto L., Henrique Gouvêa Coelho P., Mendonça Lopes A., Nestor da Silva M. and Targueta D. (2008). NEURAL NETWORKS APPLICATION TO FAULT DETECTION IN ELECTRICAL SUBSTATIONS . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 484-487. DOI: 10.5220/0001697204840487


in Bibtex Style

@conference{iceis08,
author={Luiz Biondi Neto and Pedro Henrique Gouvêa Coelho and Alexandre Mendonça Lopes and Marcelo Nestor da Silva and David Targueta},
title={NEURAL NETWORKS APPLICATION TO FAULT DETECTION IN ELECTRICAL SUBSTATIONS},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2008},
pages={484-487},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001697204840487},
isbn={978-989-8111-37-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - NEURAL NETWORKS APPLICATION TO FAULT DETECTION IN ELECTRICAL SUBSTATIONS
SN - 978-989-8111-37-1
AU - Biondi Neto L.
AU - Henrique Gouvêa Coelho P.
AU - Mendonça Lopes A.
AU - Nestor da Silva M.
AU - Targueta D.
PY - 2008
SP - 484
EP - 487
DO - 10.5220/0001697204840487