Transformer Fault Diagnosis Based on Gene Expression Programming Classifier
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.
Hao Zhang, Yang Fu and Zhong Tang
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