Fault Diagnosis for Power Grid Systems Based on Rough Set and Bayesian Network

DOI

10.2991/icmmita-15.2015.105How to use a DOI?

Keywords

Power System; Fault Diagnosis; Rough Set; Bayesian Network

Abstract

In terms of the uncertainties and incompleteness of alarm information in power grid fault diagnosis, this paper proposes a fault diagnosis method based on rough set combined with Bayesian network. Using the ability of rough set to reduce knowledge and process indeterminate information and mine fault information hierarchically, using the attribute reducing method based on cognizable matrix and information entropy, the optimal attribute reduction combination is extracted. Finally, by means of the reduction decision table formed by optimal attribute reduction combination, the Bayesian network model is built for parallel reasoning of each region, and the nodal probability is trained to achieve fault diagnosis. The experiment proves that this method can diagnose the fault rapidly and accurately, and has strong fault tolerance and adaptability.

Copyright

© 2015, 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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TY  - CONF
AU  - Baoyi Wang
AU  - Chongchong Liu
AU  - Shaomin Zhang
PY  - 2015/11
DA  - 2015/11
TI  - Fault Diagnosis for Power Grid Systems Based on Rough Set and Bayesian Network
BT  - Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 544
EP  - 549
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-15.2015.105
DO  - 10.2991/icmmita-15.2015.105
ID  - Wang2015/11
ER  -