A Novel Association Rule Mining with IEC Ratio Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers

Dissolved gas Analysis (DGA) is the most important component of finding fault in large oil filled transformers. Early detection of incipient faults in transformers reduces costly unplanned outages. The most sensitive and reliable technique for evaluating the core of transformer is dissolved gas analysis. In this paper we evaluate different transformer condition on different cases. This paper uses dissolved gas analysis to study the history of different transformers in service, from which dissolved combustible gases (DCG) in oil are used as a diagnostic tool for evaluating the condition of the transformer. Oil quality and dissolved gasses tests are comparatively used for this purpose. In this paper we present a novel approach which is based on association rule mining and IEC ratio method. By using data mining concept we can categorize faults based on single and multiple associations and also map the percentage of fault. This is an efficient approach for fault diagnosis of power transformers where we can find the fault in all obvious conditions. We use java for programming and comparative study.