Application of multi-sensor information fusion technology in the power transformer fault diagnosis

Abstract

This paper is aimed at applying BP neural network and Dempster-Shafer (D-S) evidence theory to realize the real-time monitoring and the fault diagnosis by taking power transformer as the object of fault diagnosis. We make use of the neural network's ability of better fault tolerance, strong generalization capability, characteristics of self-organization, self-learning, and self-adaptation, and take advantage of multi-source information fusion technology to realize comprehensive processing for uncertainty information. Combining with BP neural network and D-S evidence theory, a characteristic layer fusing model of power transformer fault diagnosis has been established. As high-voltage electric equipment has complex structure and works in harsh environments, the fiber bragg grating (FBG) temperature sensors are used to monitor the real-time thermal characteristics of the power transformer hotspot. The simulation results of power transformer fault diagnosis shown that this method is effective.