Abstract
Outdoor insulators in high voltage power lines serve as electrical insulation barriers and mechanical supports for live conductors. They are exposed to environmental contaminants and physical deterioration or damage. Hence, polluted insulator analysis is a fundamental concern for proper power system operation. This study harvests a comprehensive insulator surface dataset composed of 4500 images under different surface conditions: clean surface, clean surface with the water droplets, contaminated surfaces with the soil and cement, as well as a wet surface, which is mixed with the soil and cement contaminants. Convolutional neural networks (CNNs) are employed, and a systematic model selection methodology is introduced to construct an accurate classifier of insulator surface conditions while taking into consideration the potential implementation of the constructed CNN in resource-limited embedded devices. The results show the proposed model complexity reduction technique leads to a lighter architecture by a factor of ~3 at the expense of a slight reduction of 6.5% in classification accuracy.