A power network fault diagnosis method based on data mining association rules

When diagnosing the power network fault,it faces to the puzzle problem that is how to find the key data acquirement in the real complex power system fault diagnosis with mass data.And if the fault information is imperfect and indeterminate or even the key information is lost,it may result in the condition that correct conclusion could not be given by fault diagnosis.To settle these problems,the paper introduces the DLG(Direct Large itemsets Generation) algorithm based on data mining association rules to diagnose the power network faults.At first,the protections and circuit breakers are taken as conditional attributes and faulty region as decision-making attribute,various faults are investigated and decision table is established.Then by use of attribute reducing method based on data mining association rules and modifying thresholds based on interactive data mining,the optimal attribute reduction combination is directly extracted.Finally,by means of the reduction decision table formed by optimal attribute reduction combination and interactive data mining based on association rules,the diagnosis reasonings to diversified conditions are obtained.The fault diagnosis software is programmed by C programming language.Results of calculation examples show that the proposed method is correct and effective,and can improve the effectiveness of the fault diagnosis system,so this method is available.