Affiliation:
1. School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
2. Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710048, China
Abstract
Most disconnector fault diagnosis methods have high accuracy in model training. However, it is a challenging task to maintain high accuracy, a faster diagnosis speed, and less computation in practical situations. In this paper, we propose a multi-granularity contrastive learning (MG-CL) framework. First, the original disconnector current data are transformed into two different but related classes: strongly enhanced and weakly enhanced data, by using the strong and weak enhancement modules. Second, we propose the coarse-grained contrastive learning module to preliminarily judge the possibility of faults by learning the features of strongly/weakly enhanced data. Finally, in order to further judge the fault causes, we propose a fine-grained contrastive learning module. By comparing the differences in the data, the final fault type was judged. Our proposed MG-CL framework shows higher accuracy and speed compared with the previous model.
Funder
National Nature Science Foundation of China
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering