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GMSA-Net: A Transmission Line Ice Thickness Identification Network Based on Global Micro Strip Awareness
Yu Zhang1,2,3, Yinke Dou1,3,4, Yangyang Jiao3
1College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Sensors (Basel, Switzerland)
|July 13, 2024
Summary
Accurate ice thickness identification for transmission lines is crucial for resonant deicing. This study introduces a novel GMSA-Net model, achieving high accuracy in detecting ice thickness on power lines.
Area of Science:
- Electrical Engineering
- Computer Vision
- Materials Science
Background:
- Ice accumulation on transmission lines disrupts power system operations.
- Resonant deicing is an effective solution, but requires precise ice thickness data.
Purpose of the Study:
- To develop an accurate ice thickness recognition model for transmission lines.
- To enhance the efficiency and applicability of resonant deicing techniques.
Main Methods:
- Design of a Global Micro Strip Awareness Net (GMSA-Net) model.
- Introduction of Mixed Strip Convolution Module (MSCM) and global micro awareness module (GMAM) for feature extraction.
- Calculation of ice thickness via regional pixel statistics on segmented images.
Main Results:
- Achieved 96.4% mean Intersection over Union (mIoU) for image segmentation.
- Attained a balanced F-Score (F1-Score) of 98.1%.
- Ice thickness identification error was within 3.8%.
Conclusions:
- The GMSA-Net model accurately identifies transmission line ice thickness.
- The method provides a reliable basis for resonant deicing engineering applications.
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