Applying Grey Theory Prediction Model on the DGA Data of the Transformer Oil and Using It for Fault Diagnosis

摘要

This Non-Destructive Evaluation of Power transformer by monitoring various parameters, to predict its in-service behavior, is very much necessary for operating engineer to avoid catastrophic failures and costly outages. Dissolved Gas Analysis (DGA) is an important tool for transformer fault diagnosis. It is observed that the results of DGA doesn't have a perceivable change over a short period. For less population of data availability grey modeling is used. To apply probability theory, statistics, and fuzzy systems, there is a requirement for a large number of data, then only conclusion or some inference can be drawn. The advantage of using the grey system theory is that, it gives a fair accuracy in predicting the volume of the gases, expected to be generated after some time period, using a small sample of data. In this paper we have done a comparative study on the predicted results obtained by different model of Grey theory mainly whitened model, connotation model and modified grey model. It is found that the error generated from the prediction by all the three model are within the limit of 15% which is acceptable. Additional by linear regression we are establishing a correlation between the key gases. This helps us to detect the abnormality of the situation and diagnose the type of fault. Additional this paper deals with the study on the behavior of the gas dissolved in the oil which has undergone filtration and the one without filtration. Through graphical means it has been clearly shown that filtration at periodic interval will extend the life of the transformer. It has been shown that the rate of gas generation also plays an important role to detect an active fault.