Transformer Fault Diagnosis Based on Semi-supervised Classifying Method

Dissolved gas analysis(DGA) of oil in transformer is one of the most important methods to diagnose fault of power transformer.Most of existing diagnosis models will need large amount of labeled data to construct classifiers,while normally ignoring without unlabeled data,thus the semi-supervised classifying(SSC) method is introduced to build a new fault diagnosis model for power transformers.In its learning process,the SSC consider both labeled data and unlabeled data to get more knowledge,i.e.it learns better.A SSC method adopting fuzzy nearest neighborhood label propagation(FNNLP-SSC) is adopted for actual fault diagnosis of power transformers.In this method,based on the similarity connections between a sample and its K nearest data,the model classifies the unlabeled data by making the labels propagate from the labeled data to unlabeled data.Results of diagnosing a DGA sample of fault show that the proposed FNNLP-SSC method performs better than the fuzzy C-mean(FCM) method and the three ratios method of IEC,so it is concluded that the proposed method is feasible and valid for diagnoses of transformer fault.