电力变电站设备柜中与终端块相关的不规则组件的识别方法
Weiguo Cao1, Zhong Chen1, Xuhui Deng2
1School of Electrical Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了一种自动化方法,用于识别智能变电站中的终端块组件,使用先进的AI模型,如YOLOv7和PaddleOCR. 这一创新显著提高了检查效率和准确性,减少了手工劳动和潜在的错误.
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:
- 智能变电站依赖于终端块组件的手动检查,这是低效的,容易出现人为错误.
- 目前对变电站设备柜的检查方法耗时,需要大量的人力资源.
研究的目的:
- 为智能变电站开发一个自动化终端块组件检测和识别方法.
- 提高变电站设备检查流程的效率和准确性.
主要方法:
- 一个多阶段的识别系统,集成了精简的You Only Look Once版本7 (YOLOv7) 模型,微分二元化 (DB) 和PaddleOCR.
- 开发YOLOv7面积定向 (YOLOv7-AO) 系统,用于精确的终端块定位和紧的面积提取.
- 集成DB细分头部和注意力机制 (YOLOv7-DBAH) 来处理密集和不规则的组件,然后使用OCR进行身份匹配.
主要成果:
- 该YOLOv7-AO型号实现了高检测准确度,运行速度快4.45倍,并具有良好的便携性.
- YOLOv7-DBAH模型表现出卓越的性能,F1得分从0.83增加到0.89,精度超过0.91.
- 拟议的方法成功地识别了终端块组件,显示了实际适用性.
结论:
- 开发的自动化方法显著提高了智能变电站终端块组件检查的效率和准确性.
- 将YOLOv7变体与DB和OCR集成,为工业环境中复杂的组件识别任务提供了强大的解决方案.
- 该系统适合实际应用,为现代化变电站维护提供了有价值的工具.
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