Transformer Fault Diagnosis Based on Gene Expression Programming Classifier

Abstract:

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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)

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 and 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:

$35.00

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