Method of Power Grid Fault Diagnosis Based on Feature Mining
Ninghui Zhu · Proceedings of the CSEE · 2010
The two main bottlenecks in the application of expert system are: the maintenance of rule base; the coordination of speed and accuracy of reasoning. Features and key events of fault events were analyzed, then a novel method of association rule mining based on feature mining was presented, the method was originated from frequent pattern (FP)-algorithm and was improved. The improvements include: features of fault information are utilized, such as the time sequence and causality of events, fault type and serious fault or unusual fault; OR logical function of rules is added; prune technique of FP-tree is improved. Use case shows the improved algorithm could reduce invalid mining largely, and the speed and accuracy of the reasoning is heightened prominently. The algorithm is fit for being used online.