Paper Title:
Research of Power Transformer Fault Diagnosis System Based on Rough Sets and Bayesian Networks
  Abstract

As one of the most important electric equipment for reliable power supply, the secure operation of power transformer must be guaranteed. Three-ratio method based on the Dissolved Gases Analysis (DGA) is most widely used for transformer fault diagnosis currently. Its advantage is simple and easy to use, but its encoding is incomplete and the faults classification zone is over absolute. This paper combines rough sets and Bayesian Network. Rough sets is used to get useful characters, simplify data sets, obtain simplification rules and the minimum property sets; Bayesian Network is used to analyze the faults caused by uncertain elements in complex system. The fault diagnostic model is built by Bayesian Network Tool (BNT) in MATLAB, and the simulation result shows the validity of this method.

  Info
Periodical
Chapter
Chapter 2: Mechanical Engineering, Control Engineering and Materials Engineering
Edited by
Jun Hu and Qi Luo
Pages
524-529
DOI
10.4028/www.scientific.net/AMR.320.524
Citation
Q. Li, Z. B. Li, Q. Zhang, "Research of Power Transformer Fault Diagnosis System Based on Rough Sets and Bayesian Networks", Advanced Materials Research, Vol. 320, pp. 524-529, 2011
Online since
August 2011
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Price
$32.00
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