GMSA-Net:一个基于全球微条带意识的传输线冰厚识别网络
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
概括
精确识别输电线路的冰厚对于共振脱冰至关重要. 这项研究引入了一种新的GMSA-Net模型,在检测电力线路上的冰厚方面实现了高精度.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
背景情况:
- 在输电线路上积累的冰块破坏了电力系统的运行.
- 响应脱冰是一种有效的解决方案,但需要精确的冰厚数据.
研究的目的:
- 为输电线路开发一个准确的冰厚识别模型.
- 提高共振脱冰技术的效率和适用性.
主要方法:
- 全球微条带意识网络 (GMSA-Net) 模型的设计.
- 引入混合带卷积模块 (MSCM) 和全球微观意识模块 (GMAM) 进行特征提取.
- 通过对细分图像的区域像素统计计算冰厚.
主要成果:
- 在图像细分方面实现了96.4%的平均交叉点在欧盟 (mIoU).
- 获得了98.1%的平衡F-Score (F1-Score).
- 冰厚识别错误在3.8%以内.
结论:
- 该GMSA-Net模型准确地识别了输电线路的冰厚.
- 该方法为共振脱冰工程应用提供了可靠的基础.
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