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Mathematical Problems in Engineering
Volume 2014, Article ID 847623, 8 pages
Research Article

Fault Diagnosis of Oil-Immersed Transformers Using Self-Organization Antibody Network and Immune Operator

School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China

Received 2 July 2014; Accepted 18 August 2014; Published 16 November 2014

Academic Editor: Yoshinori Hayafuji

Copyright © 2014 Liwei Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


There are some drawbacks when diagnosis techniques based on one intelligent method are applied to identify incipient faults in power transformers. In this paper, a hybrid immune algorithm is proposed to improve the reliability of fault diagnosis. The proposed algorithm is a hybridization of self-organization antibody network (soAbNet) and immune operator. There are two phases in immune operator. One is vaccination, and the other is immune selection. In the process of vaccination, vaccines were obtained from training dataset by using consistency-preserving K-means algorithm (K-means-CP algorithm) and were taken as the initial antibodies for soAbNet. After the soAbNet was trained, immune selection was applied to optimize the memory antibodies in the trained soAbNet. The effectiveness of the proposed algorithm is verified using benchmark classification dataset and real-world transformer fault dataset. For comparison purpose, three transformer diagnosis methods such as the IEC criteria, back propagation neural network (BPNN), and soAbNet are utilized. The experimental results indicate that the proposed approach can extract the dataset characteristics efficiently and the diagnostic accuracy is higher than that obtained with other individual methods.