High Voltage Insulator Surface Evaluation Using Image Processing
This addresses a domain-specific problem for power utilities and transmission companies by enabling real-time inspection, though it appears incremental as it builds on existing image processing techniques.
The study tackled the problem of monitoring high voltage insulator surfaces for snow, ice, and water contamination to prevent operational breakdowns, achieving a recognition accuracy rate of 87% using a statistical approach with Standard deviation filters.
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.