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.

Study on the Analysis of Fault Data During Intelligent Operation of Power Equipment With Cluster Analysis

“…With the advancement of data mining technology, many methods have been applied in the fault data analysis of power equipment [3]. Liu et al [4] studied the fault analysis of distributed power grids based on the Bayesian algorithm combined with evidence theory and found through simulation experiments that the method had high fault tolerance and performed well in fault analysis under the situation of incomplete information. Sharan et al [5] designed a method based on spectrum analysis to monitor open-circuit faults in grid-connection and verified the effectiveness of the method by simulation analysis.…”