High Voltage Insulator Surface Evaluation Using Image Processing

TL;DR

This paper presents a real-time image processing technique for inspecting high voltage insulators, detecting snow, ice, and water to prevent operational failures in power transmission.

Contribution

A novel image processing method using Gabor and Standard deviation filters for insulator surface condition assessment is introduced, achieving high recognition accuracy.

Findings

Recognition accuracy of 87% with Standard deviation filter.

Effective detection of snow, ice, and water on insulators.

Potential for real-time insulator monitoring in power systems.

Abstract

High voltage insulators are widely deployed in power systems to isolate the live- and dead-part of overhead lines as well as to support the power line conductors mechanically. Permanent, secure and safe operation of power transmission lines require that the high voltage insulators are inspected and monitor, regularly. Severe environment conditions will influence insulator surface and change creepage distance. Consequently, power utilities and transmission companies face significant problem in operation due to insulator damage or contamination. In this study, a new technique is developed for real-time inspection of insulator and estimating the snow, ice and water over the insulator surface which can be a potential risk of operation breakdown. To examine the proposed system, practical experiment is conducted using ceramic insulator for capturing the images with snow, ice and wet surface conditions. Gabor and Standard deviation filters are utilized for image feature extraction. The best achieved recognition accuracy rate was 87% using statistical approach the Standard deviation.