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

Abstract EN

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.