Paper Title:
Transformer Fault Diagnosis Based on Gene Expression Programming Classifier
  Abstract

Gene Expression Programming (GEP), which is suitable for transformer fault diagnosis Classification, is combined with transformer oil dissolved gas analysis (DGA), and also a method of transformer fault diagnosis based on self-adaptive GEP classification algorithm is proposed. We choose 400 groups of DGA measured data which includes a variety of failure and does not redundant as the training samples and test samples of the GEP classifier. A large number of diagnostic examples show that the proposed self-adaptive classification GEP is suitable for transformer fault diagnosis, and its performance is better than using Naive Bayes (NB) classifier, BP network and Immune classification.

  Info
Periodical
Advanced Materials Research (Volumes 354-355)
Chapter
Chapter 6: Power System and Automation
Edited by
Hao Zhang, Yang Fu and Zhong Tang
Pages
1022-1026
DOI
10.4028/www.scientific.net/AMR.354-355.1022
Citation
Z. Dong, Y. L. Zhu, "Transformer Fault Diagnosis Based on Gene Expression Programming Classifier", Advanced Materials Research, Vols. 354-355, pp. 1022-1026, 2012
Online since
October 2011
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Price
$32.00
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