Transmission Line Insulator Defect Detection and Diagnostics Using Machine Learning Models

The reliability of power transmission systems is highly dependent on the condition of transmission line components, particularly insulators, whose failure can lead to outages, safety risks, and increased maintenance costs. In large transmission networks, traditional manual patrolling practices are increasingly challenged by the scale of assets, difficult terrain, and dependency on individual experience for defect identification. While drone-based patrolling has improved inspection coverage and data quality, it has simultaneously resulted in the generation of very large volumes of image data that are difficult to analyse using conventional methods. This paper presents the application of Artificial Intelligence (AI) and Machine