Early Fault Detection of Outdoor Insulators using Digital Image Classification

Electrical Insulators are commonly found near electrical installations and they provide safety to the operators and electrical installations which could be operating at high voltage. The failure of such insulator is unwarranted and can cause severe problems threating life and property. Manual inspection of all outdoor insulators is tedious and cumbersome since these insulators are mounted at a height of 10 – 15 feet above the ground level. Inspecting all insulation for scrapes and breaks is essential. The purpose of this paper is to facilitate early detection of the damaged insulator using Image processing technology. Good and bad insulators are classified by this technique. The study is carried out on images of the pin and disk insulators. This technology is based on a deep learning method. The CNN model used in the paper shows validation accuracy of 100% and test accuracy of 96%. This technique can be implemented for early detection of failure of electrical apparatus such as switch gear and batteries.