Foreign Object Obstruction Evaluation for Distribution Network Inspection

Moheddin U. Sumagayan

doi:10.1109/ACCESS.2024.3484156

2024-01-01

Moheddin U. Sumagayan *

Earl Ryan M. Aleluya

Rohanni B. Mangorsi

Francis Jann A. Alagon

Edward Rowe M. Aleluya

Ysmael Alongan B. Mangorsi

H. Waruna H. Premachandra

Chinthaka Premachandra

Hiroharu Kawanaka

DOI：10.1109/ACCESS.2024.3484156

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摘要

En 中文

Power outages impede economic operations as numerous processes rely on electricity. The solution is fewer interruptions through maintenance and inspection. However, traditional foot patrol inspection methods could be more complex with risks, labor intensiveness, and time consumption. An innovative contemporary alternative involves inspecting distribution networks by unmanned aerial vehicles (UAVs). Thus, this paper introduces a three-tier inspection procedure: track power lines, detect utility assets (transformer bank, low-voltage bushing, high-voltage bushing, arrester, radiator fin, and cutoff fuse), and spot foreign object obstructions (snake, gecko, and tree branch). This work exhibits two contributions. First, it introduces new learning representations for detecting power lines, utility assets, and foreign obstructions. The image processing-based techniques are built by modifying pre-trained neural networks - Point Instance Network (PINet) for power lines and You Only Look Once (YOLO) for utility assets and obstructions. Second, it presents trained detection models with image datasets tailored to each problem. The datasets consist of images from both obstruction-free and shelled UAVs for the three detection tasks. Experimental results indicate no significant disparity in utilizing two distinct UAV variants, with or without a shell. The proposed method, leveraging Transfer Learning with PINet, yields around 80% accuracy and 72% f1-score across obstruction-free and obstructed image datasets. Moreover, the retrained YOLO checkpoints with yolov5m and yolov5l weights showcase suitability for deployment in detecting utility assets and foreign object obstructions that achieved mAPs around 0.96 and 0.85, respectively. Despite a slight decline in performance metrics with obstructed images, the models signify a balance between detection accuracy and the safety conferred by the insulated shell.

Keyword:

Inspection

Distribution networks

Drones

Navigation

YOLO

Autonomous aerial vehicles

Poles and towers

Insulators

Visualization

Manuals

Deep learning