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Transformer Fault Diagnosis Based on Gene Expression Programming Classifier

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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.

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Periodical:

Advanced Materials Research (Volumes 354-355)

Pages:

1022-1026

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.354-355.1022

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Cite this paper

Online since:

October 2011

Authors:

Zhuo Dong, Yong Li Zhu

Keywords:

Data Normalization, DGA, Gene Expression Programming (GEP), Transformer Fault Diagnosis

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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