Transformer Fault Diagnosis Based on Gene Expression Programming Classifier

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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.