Insulator Pollution Degree Identification Method Based on Sampling Cloth and Layered Idea

The insulator that plays an insulating role at one end of the high-voltage line is exposed to the air environment, and long-term pollution accumulation in a humid environment will cause flashover between the insulators, which will affect the power supply line and require greater cost to detect and repair. At present, the contact measurement method is used to detect the pollution on insulator surface by cleaning, and the non-contact measurement method uses the idea of characteristic band imaging, which can guide the regular cleaning of insulator surface. In this paper, a method is proposed to use layering ideas and machine learning algorithms on hyperspectral image of a sampling cloth wiped clean from the insulator surface to achieve insulator pollution detection. Hyperspectral data is a three-dimensional data cube with a large amount of data. Firstly, the idea of stratification is used to identify the interval where the ratio of non-soluble deposit density (NSDD) and equivalent salt deposit density (ESDD) of insulators is located, and then XGBoost machine learning algorithm is used to establish the regression detection model of salt density and grey density in the corresponding interval. The method has been proven to have high accuracy and feasibility, and can guide the regular cleaning of insulator surface pollution in practical applications.