Automated Thermal Fault Detection in Ultra-High Voltage Substation Equipment

Inspection of ultra-high voltage substations (UHVS) plays a crucial role for ensuring the stability of power grids and preventing damage that can potentially lead to serious power loss. Automating the inspection process provides early detection of equipment faults, improved safety and cost efficiency. This paper presents an automated visual inspection system, using an RGB and thermal camera mounted on an autonomous ground robot. Our approach leverages component detection in RGB images with YOLOv11n, followed by multi-modal image matching to locate components in thermal imagery and a rule-based anomaly detection algorithm. Experimental results in an operational power substation demonstrate the system’s ability to detect early-stage thermal anomalies, highlighting its potential for improving substation reliability and operational safety.