Background: To make up for the deficiency existing in single method for transformer fault
diagnosis, a model of multi-algorithm fusion based on improved Dempster-Shafer (D-S) evidence
theory was proposed through analyzing the implementation process of quantum particle swarm
optimized BP neural network (QPSO-BP).
Methods: According to the failure modes of transformer, the primary fault diagnosis was achieved
using a model group formed by several single methods, such as QPSO-BP, the inertia weight PSO
optimized BP network (IWPSO-BP) and the constriction factor PSO optimized BP network (CFPSOBP),
then the fusion decision was implemented by D-S theory. In view of the defect of standard D-S
which can not synthesize the highly conflicting evidences, the credibility factor was used to improve the
capability of information fusion.
Results: Diagnostic results show that, compared with the single models and standard D-S, the proposed
method has stronger fault tolerance, and improves the accuracy of transformer fault diagnosis.
Conclusion: The method based on the multi-algorithm fusion can enhance effectively the diagnostic
efficacy, and suitable for the pattern recognition of transformer fault.