Fault Diagnosis Of Large Power Networks Based On United Rules Mining

Considering the limitations of traditional artificial intelligence technology in the fault diagnosis of large scale power networks, distributed method was used to solve the problem. A novel united rules mining algorithm based on rough set theory was proposed for extracting united rules from distributed information system. In order to diagnose the faults better, a model of distributed fault diagnosis of power networks was constructed based on the algorithm. The faults in local power networks and the faults of tie lines between local power networks can be efficiently diagnosed by using the model, which has made up for the deficiency of fault diagnosis rules for tie lines in distributed fault diagnosis. Various complex faults can be identified efficiently. The decision table for sub-networks is greatly simplified by dividing the large scale power network into desired number of connected sub-networks. Meanwhile the united rules mining algorithm can significantly reduce the complexity of the rule extraction and solve the bottlenecks that the rough set theory encounters in the application to large power networks diagnosis.