An Augmented Naive Bayesian Power Network Fault Diagnosis Method Based on Data Mining

Bayesian Networks is used to study and deal with the reasoning under uncertainty in the power system fault process. Data mining method can find useful information for decision-making from massive history data. Therefore, an Augmented Naive Bayesian power network fault diagnosis method based on data mining is proposed to diagnose faults in power network. The status information of protections and circuit breakers are taken as conditional attributes and faulty region as decision-making attribute. Results of calculation examples demonstrated that the proposed method is correct and effective, and can improve the fault tolerance capability of the fault diagnosis system while the kernel attribute is lost, so this method is available.