Abstract
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.
Highlights
The normal operation of the transformer is conducive to the safety and stability of the power grid, and the transformer insulation system is the key to ensuring its normal operation [1,2]
This indicates that either using box dimension or multifractal spectrum alone cannot well characterize the aging state of insulating paper, since the box dimension focuses on the overall texture complexity of the image, and the multifractal spectrum focuses on the local texture complexity of the image
Such change can be reflected in the texture complexity of surface roughness, which is very similar to the judgment of the degree of wear of the object in machining
Summary
The oil-immersed transformer is the key equipment of the power distribution system. The normal operation of the transformer is conducive to the safety and stability of the power grid, and the transformer insulation system is the key to ensuring its normal operation [1,2]. Digital image processing techniques used in the textile and paper industry have gradually become one of the tools to judge the aging state of insulating paper in the maintenance link [8,9] These beneficial attempts provide a good research idea for online detection and condition evaluation of insulating paper of large power transformers. The pre-processing effect is verified by constructing the gray surface of typical images picked from different stages With these treated microscopic images of insulating paper surface available, feature extraction and characterizing of insulating paper with fractal dimension is conducted, where.
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