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

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.