Fault Detection in Power Distribution Systems Using Sensor Data and Hybrid YOLO with Adaptive Context Refinement

• Published in: Applied sciences Vol. 15; no. 16; p. 9186
• Main Authors: Scapinello Aquino, Luiza, Rodrigues Agottani, Luis Fernando, Seman, Laio Oriel, Cocco Mariani, Viviana, Coelho, Leandro dos Santos, González, Gabriel Villarrubia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2025
• Subjects: Accuracy; Analysis; Automation; Computer vision; convolutional neural network; Deep learning; Defects; Efficiency; Electric power systems; Electricity distribution; Fault diagnosis; insulator; multi-scale analysis; Neural networks; object detection; Sensors; Unmanned aerial vehicles
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15169186

Ensuring the reliability of power transmission systems depends on the accurate detection of defects in insulators, which are subject to environmental degradation and mechanical stress. Traditional inspection methods are time-consuming and often ineffective, particularly in complex aerial environments. This paper presents a fault detection framework that integrates the YOLOv8 object detection model with an Adaptive Context Refinement (ACR) mechanism. YOLOv8 provides real-time detection, while ACR incorporates multi-scale contextual information surrounding detected objects to improve classification and localization. The system is evaluated across 25 YOLO model variants (YOLOv8 to YOLOv12) using high-resolution UAV datasets from operational power distribution networks. Results show that ACR improves mean Average Precision (mAP) in all cases, with gains of up to 22.9% for YOLOv10n (from 0.556 to 0.684 mAP) and average improvements of 12.6% for YOLOv10, 8.6% for YOLOv12, 5.6% for YOLOv9, and 4.0% for YOLOv8. The method maintains computational efficiency and performs consistently under varied environmental and fault conditions, making it suitable for the real-time UAV-based inspection of power systems.