Diagnosing and Classifying the Fault of Transformer with Deep Belief Network

Abstract

As an important equipment of smart grid, transformer fault has a great impact on the safe and stable operation of smart grid, and therefore the transformer fault diagnosis and classification become particularly critical. This paper first introduces the application of restricted Boltzmann machine and deep belief network in transformer fault diagnosis and classification, then designs a transformer fault diagnosis and classification model based on rectified linear unit and deep belief network for a large number of transformers in smart grid, and describes in detail the selection of feature parameters, the partition of fault patterns, the analysis of sample data and the setting of model parameters in the proposed model. Finally, the efficiency and accuracy of the proposed model are tested and compared with SVM and BPNN by using the actual transformer fault data collected in daily operation. The case study shows that the proposed model can effectively achieve the transformer fault diagnosis and classification, and provides a valuable method for the transformer fault diagnosis and classification.

This work was funded by the 2017 Science and Technology Project of SGCC (GYB17201700204): “Research of Fault Diagnosis and Maintenance Assistive for Transmission and Distribution Equipment Based on Big Data Technology”.