The Characteristic Analysis of ESDD and NSDD Detection of Composite Insulators Based on Hyperspectral Technology

Abstract

Pollution flashover of insulators has been viewed as a common issue existing in the area of power systems for a long term. However, traditional detection methods such as equivalent salt density and leakage current (LC) can hardly satisfy the requirements of the progressing smart grid. Herein, a novel highly-efficient method is proposed for classifying the pollution degree detection of insulators based on hyperspectral technology. The insulating sheets and actual insulators were set as the research object and their hyperspectral images are obtained. Moreover, the data for describing the characteristic wavelengths and the G color components of the samples were extracted from the images as the characterization of the equivalent salt deposit density (ESDD) and nonsoluble deposit density (NSDD) respectively. The ESDD and NSDD detection model is established based on the multiple linear regression (MLR) algorithm and the random forest (RF) algorithm. Ultimately, the proposed model reaches a detection accuracy of above 84% on the insulating sheets and 75% on the actual insulators, which can be viewed as acceptable. The hyperspectral imaging technique has considerable potential for the non-contact detection of ESDD and NSDD.