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The insulating paper of the transformer is affected by many factors during the operation, meanwhile, the surface texture of the paper is easy to change. To explore the relationship between the aging state and surface texture change of insulating paper, firstly, the thermal aging experiment of insulating paper is carried out, and the insulating paper samples with different aging times are obtained. After then, the images of the aged insulating paper samples are collected and pre-processed. The pre-processing effect is verified by constructing and calculating the gray surface of the sample. Secondly, the texture features of the insulating paper image are extracted by box dimension and multifractal spectrum. Based on that, the extreme learning machine (ELM) is taken as the classification tool with texture features and aging time as the input and output, to train the algorithm and construct the corresponding relationship between the texture feature and the aging time. After then, the insulating paper with unknown aging time is predicted with a trained ELM algorithm. The numerical test results show that the texture features extracted from the fractal dimension of the micro image can effectively characterize the aging state of insulating paper, the average accuracy can reach 91.6%. It proves that the fractal dimension theory can be utilized for assessing the aging state of insulating paper for onsite applications.

Fractal Dimension Analysis Based Aging State Assessment of Insulating Paper with Surface Microscopic Images