A Power System Fault Diagnosis Method Using Temporal Bayesian Knowledge Bases
Sun Mingwe · Power System Technology · 2014
After the fault occurs in power grid,many alarm messages are generated. For power system fault diagnosis,it is important to utilize the alarms and their temporal information,and deal with the uncertainty such as mal-function,rejection and incompletion. The theory of temporal Bayesian knowledge bases(TBKB) can clearly express the temporal constraint relationship among multiple events,and possess Bayesian network's reasoning ability. The TBKB-based power system fault diagnosis models were studied. The expression of temporal casual relationship(TCR) among fault components and protection operations and related breakers tripping are proposed. The consistency checking of TCR was studied,as well as on-line searching algorithm of suspicious components and automatic generating method of TBKB models. For the states of information missing,the state assumption is adopted to create hypothetic state combinations. For these states,Bayesian backward and forward reasoning is made to detect the fault component and identify mal-function and rejection of protections and breakers. The given examples have illustrated that the proposed fault diagnosis method is effective.