To deal with the deficiencies of existing insulating paper state detection methods

a new non-destructive detection method is proposed

in which the texture characteristics of the captured insulating paper images are adopted to evaluate its aging state. Firstly

the insulating papers in different aging stages are collected and preprocessed

and its texture feature value is obtained by calculating the gray-level run-length matrix of the image. Then

correlation analysis is used for filtering the features that are highly correlated with the degree of polymerization. Based on this

the support vector machine is used for verifying the effectiveness of the selected features to characterize the aging state of the insulating paper. Finally

the multiple linear regression method is used to obtain the fitting relationship between the texture features and the degree of polymerization

and it is verified by the actual measured results. The results show that the measured texture characteristics of insulating paper have a good fitting relationship with the degree of polymerization

and the error rate between predicted value and actual value is less than 10%

which verifies the feasibility and effectiveness of this method for evaluating the aging state of insulating paper. The method

combined with the telescopic endoscope

can extend the theoretical method to practical application

and it can bring some convenience for the future transformer maintenance.