Fault diagnosis of transformer using association rule mining and knowledge base

Association rule mining makes interesting associations and/or correlations among large sets of data. Those associations can be refined as decision rules to be used and stored in a knowledge base system. In this paper, an approach based on association rule and knowledge base is proposed and implemented in the fault diagnosis of a transformer system. According to the features of association rule, the Apriori algorithm is adopted and modified to generate decision rules from power transformer information for building knowledge base, then the rules can be refined to diagnose the fault of the transformer through reasoning, and a prototype system is developed. This approach based on association rule is described in detail and the application is illustrated by an example. A comparison with the IEC (International Electrotechnical Commission) three-ratio method shows the proposed method can provide better accuracy in performance.