Power Transformer Fault Diagnosis Using RST and NBN
Min Yang · Gao dianya jishu · 2009
If the fault information of power transformer is incomplete or indeterminate or even the key information is lost when power transformer goes wrong,correct conclusion could not be given by fault diagnosis.To settle this problem,we proposed a new power transformer fault diagnosis method in which the rough set(RS)theory is well integrated with Native Bayesian Network(NBN).First,the results of dissolved gas-in-oil analysis(DGA)and conventional electrical tests were taken as conditional attributes and the faulty region was taken as decision attributes.Various connection relations between fault and symptom were investigated and decision table was established.Next,the optimal attribute reduction combination can be obtained by using attribute reducing method based on cognizable matrix and information entropy to simplify expert knowledge and to reduce fault symptoms.Then,the Native Bayesian Networks model was established according to the reduction decision table formed by the optimal attribute reduction combination,and the nodal probability is trained.Finally,the correctness and effectiveness of this method were validated by the results of practical fault diagnosis examples.