The analysis of dissolved gas in oil can provide an important basis for transformer fault diagnosis.In order to improve the accuracy of transformer fault diagnosis,a method based on the relational teacher-student netw...The analysis of dissolved gas in oil can provide an important basis for transformer fault diagnosis.In order to improve the accuracy of transformer fault diagnosis,a method based on the relational teacher-student network(R-TSN)is proposed by analyzing the relationship between the dissolved gas in the oil and the fault type.R-TSN replaces the original hard labels with soft labels,and uses it to measure the similarity between different samples in the space,to a certain extent,it can obtain the hidden feature information in the samples,and clarify the classification boundary.Through the identification experiment,the effect of R-TSN diagnosis model is analyzed,and the influence of the compound fault of discharge and thermal on the diagnosis model is studied.This paper compares R-TSN with support vector machines(SVMs),decision trees and multilayer perceptron models in transformer fault diagnosis.Experimental results show that R-TSN has better performance than the above methods.After adding compound faults in the sample set,the accuracy rate can still reach 86.0%.展开更多
基金supported by Open Fund of Beijing Key Laboratory of Research and System Evaluation of Dispatching Automation Technology,China Electric Power Research Institute(SGDK 0000DZQT2003377)。
文摘The analysis of dissolved gas in oil can provide an important basis for transformer fault diagnosis.In order to improve the accuracy of transformer fault diagnosis,a method based on the relational teacher-student network(R-TSN)is proposed by analyzing the relationship between the dissolved gas in the oil and the fault type.R-TSN replaces the original hard labels with soft labels,and uses it to measure the similarity between different samples in the space,to a certain extent,it can obtain the hidden feature information in the samples,and clarify the classification boundary.Through the identification experiment,the effect of R-TSN diagnosis model is analyzed,and the influence of the compound fault of discharge and thermal on the diagnosis model is studied.This paper compares R-TSN with support vector machines(SVMs),decision trees and multilayer perceptron models in transformer fault diagnosis.Experimental results show that R-TSN has better performance than the above methods.After adding compound faults in the sample set,the accuracy rate can still reach 86.0%.
