Advance analytics using artificial intelligence (AI) in unmanned aerial system (UAS) for defect detection in MV overhead line system distribution network TNB

This paper explores the integration of six Artificial Intelligence (AI) models for defect detection in Medium Voltage (MV) overhead lines using Unmanned Aerial Systems (UAS) and AI-assisted thermal analysis. The deployment of AI models within UAS has demonstrated an accuracy of 86% in defect detection and a processing speed increase of up to 15 times compared to conventional methods. AI-enhanced thermal analysis further improves detection by automatically identifying hotspots and performing Delta T calculations to streamline the analysis process. This integration has led to significant advancements, including enhanced pattern recognition, improved inspection speed and throughput, consistent and standardized results, and predictive maintenance capabilities. These advancements address the limitations of conventional inspection methods and offer a more efficient and reliable approach to monitoring MV overhead line systems in Distribution Networks, TNB.