HIGH VOLTAGE INSULATOR SURFACE MONITORING SYSTEM USING IMAGE PROCESSING AND MACHINE LEARNING

Abstract

High voltage power distribution lines play a fundamental role in electricity transmission. They are quite essential to transmit the electrical power from the generation units to the various distribution stations, and their proper functioning being vital for the operation of power grids. High voltage insulators are the essential components that serve as electrical insulation and mechanical support for live conductors. Since the insulator surfaces are physically unprotected and regularly exposed to severe weather conditions, their monitoring and maintenance become a fundamental concern for the power transmission companies. In this thesis, a comprehensive literature review over previously established industrial methods in the monitoring of outdoor insulators is conducted to address this problem. Then, state-of-the-art and current research methods along with advanced intelligent techniques are discussed. A new methodology to evaluate the outdoor insulation surface is introduced and discussed, and in-depth analysis using different classifiers is conducted over data obtained from ceramic, glass, and polymer insulators in various weather conditions. A dataset composed of 3000 high voltage outdoor insulator images with different surface conditions such as clean surface, contamination with soil, cement, and water is created to construct an insulator surface classifier model. Utilizing recent techniques in deep learning, this thesis proposes the following methods: brute-force model selection, pre-trained CNN, and model optimization steps applied to the best model selected from the brute-force model selection technique. Finally, keeping in mind the possible implementation of the best selected CNN in resource-limited embedded devices, a model complexity reduction technique is applied to decrease the storage and computation process by training only the Dense classifier layers. Ultimately, the technique employed in this thesis can distinguish a normal insulator from a contaminated insulator and advise the type of pollution. The thesis results show that the brute-force model selection method can adequately construct a highly efficient solution with 90.2% accuracy on the unseen test dataset. Also, the optimization technique employment led to three times lighter architecture at the expense of ~4.9% reduction in the accuracy.