Fault Diagnosis of Power Transformers Using Rough Set Theory

This paper has presented an effective and efficient approach to extract diagnosis rules from inconsistent and redundant data set of power transformers using rough set theory. The extracted diagnosis rules can effectively reduce space of input attributes and simplify knowledge representation for fault diagnosis. The fault diagnosis decision table is first built through discretized attributes. Next, the genetic algorithm based optimization process is used to obtain the minimal reduct of symptom attributes. Finally, the rule simplification process is adapted to achieve the maximal generalized decision rules derived from inconsistent and redundant information. Experimental results demonstrate that the proposed approach has remarkable diagnosis accuracy than the existing method.