Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

Research on Power System Fault Diagnosis Based on Bayesian Networks

Authors
Guofeng Yang, Qingming Xiao, Hong Ouyang, Jiakui Zhao, Tingshun Li, Jing Zhou
Corresponding Author
Guofeng Yang
Available Online March 2013.
DOI
10.2991/iccsee.2013.638How to use a DOI?
Keywords
power system, fault diagnosis, Bayesian network, structure learning, parameter learning.
Abstract

Aiming at the incompleteness and uncertainty of information existing in power system fault diagnosis, a new fault diagnosis approach based on Bayesian network is proposed in this paper. Through the Bayesian network of structure learning and parameter learning, a power system fault diagnosis model based on Bayesian network has been proposed. Conditional probability table describes the connection degree between various factors in quantity. Diagnostic results of instance proved the effectiveness and superiority of the proposed method.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
Series
Advances in Intelligent Systems Research
Publication Date
March 2013
ISBN
978-90-78677-61-1
ISSN
1951-6851
DOI
10.2991/iccsee.2013.638How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Guofeng Yang
AU  - Qingming Xiao
AU  - Hong Ouyang
AU  - Jiakui Zhao
AU  - Tingshun Li
AU  - Jing Zhou
PY  - 2013/03
DA  - 2013/03
TI  - Research on Power System Fault Diagnosis Based on Bayesian Networks
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2552
EP  - 2555
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.638
DO  - 10.2991/iccsee.2013.638
ID  - Yang2013/03
ER  -