Distributed Power Grid Fault Diagnosis Based on Naive Bayesian Network and D-S Evidence Theory

Abstract

On the basis of Bayesian network, evidence theory is introduced, a kind of distributed power network fault diagnosis method based on Naive Bayesian network and D-S evidence theory is proposed. Firstly, the fault area is determined by the real-time connection method, the fault region is segmented by the butterfly segmentation method. Secondly, according to the historical fault samples, the decision table is established, and knowledge reduction based on Rough Set Theory. The naive Bayesian network model is constructed according the best combination, and the probability of each node is trained. Finally, D-S evidence fusion is performed on the fault component diagnosis information in the overlap between the subnets. The simulation results show that the proposed method can reduce the complexity of modeling and improve the fault tolerance of the system in the condition of incomplete information, and has good diagnosis results.