Fault Diagnosis of SF_6 Circuit Breaker Using Rough Set Theory and Bayesian Network
Shuai Liu · Gao dianya jishu · 2009
In order to find the reason quickly and accurately why the circuit breaker faults occur,an HV SF6 fault diagnosis method based on rough sets theory(RST) and Bayesian network(BN) is presented.First,according to the fault sample sets of the circuit breaker,the relationship between fault symptom sets and fault reason sets are found.Thus,the fault diagnosis decision table of circuit breaker is established.Second,the discernible matrix algorithm of attribute reduction in the RST is used to reduce the attribute of this fault diagnosis decision table and to eliminate the redundancy knowledge.Then the minimal diagnostic rules can be obtained after simplify the expert knowledge.The complexity of BN structure can be decreased effectively based on the minimal rules.Finally,probability reasoning can be realized by BN,which can be used to analyze fault reasons of circuit breaker fleetly.This method is proved to be feasible and effective for the HV SF6 circuit breaker by the result of practical fault diagnosis examples.The results can provide maintenance basis for the circuit breaker maintenance.