Pixel-Level Classification of Pollution Severity on Insulators Using Photothermal Radiometry and Multiclass Semisupervised Support Vector Machine

Abstract

Pollution flashover is a serious accident with a wide range of impacts in power grid. Accurate and timely classification of pollution severity is both a key to preventing pollution flashovers and a crucial challenge. This article proposes an innovative method based on photothermal radiometry (PTR) and multiclass semisupervised support vector machine for the classification of insulator pollution severity. The introduction of time-dimension information makes it possible to achieve pixel-level image identification instead of image- or region-level image identification. The PTR physical model for pollution severity measurement is established to determine the effect of pollution severity parameters such as the equivalent salt deposit density and the nonsoluble deposit density on the transient and frequency-domain thermal radiation characteristics of contamination layer. The relevant features are extracted by using principal component analysis. A semisupervised classifier is proposed to solve the problem of poor generalization due to insufficient labeled samples in industrial applications. Experimental results verify the satisfactory efficiency and accuracy of the proposed method and an estimation framework for fast, accurate, and nondestructive industrial application without the tedious work of labeling large amounts of data is concluded.