基于RDIDSNet的绝缘体放电严重性评估算法
Cheng Chi1, Li Keyu2, Yanhui Meng1
1State Grid Hebei Electric Power Co. Ltd, Ultra High Voltage Branch, Shijiazhuang, 050000, Hebei, China.
Scientific reports
|April 8, 2025
概括
本研究介绍了RDIDSNet,这是一个增强的YOLOv8算法,用于评估绝缘体放电严重程度. 它在边缘设备上以61FPS达到78.1%的准确性,使得快速精确的线路检查成为可能.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 绝缘器放电对电力线路的可靠性构成重大风险.
- 准确的,实时评估绝缘体放电严重性对于有效的维护至关重要.
- 现有的方法可能缺乏现场边缘计算所需的速度或准确性.
研究的目的:
- 开发一种改进的绝缘体放电严重性评估算法.
- 为了提高检测准确度和速度,使用边缘计算和紫外线摄像头.
- 为电力线路检查提出一种轻量级但功能强大的深度学习模型.
主要方法:
- 提出了基于YOLOv8框架的改进评估算法.
- LDConv取代了标准卷积,用于增强轻量级的特征提取.
- 集成了ACMix注意力机制,以改善目标焦点,以最小的性能损失.
- 形状IOU损失功能取代了CIOU,以提高检测准确度.
主要成果:
- 拟议的RDIDSNet算法实现了78.1%的检测精度.
- 该算法以每秒61 (FPS) 的快速检测速度运行.
- 与原来的YOLOv8算法相比,RDIDSNet表现出卓越的性能.
结论:
- RDIDSNet算法为绝缘体放电严重性评估提供了一个快速而准确的解决方案.
- 该算法适合在边缘设备上部署,以实时检查电力线路.
- 拟议的改进有效地提高了特征提取,目标聚焦和检测精度.
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