基于残留学习的机器人图像分析模型用于低压分布式光伏故障识别和定位
Xudong Zhang1, Yunlong Ge1, Yifeng Wang1
1State Grid Hebei Electric Power Company, Shijiazhuang, China.
Frontiers in neurorobotics
|May 8, 2024
概括
本研究介绍了一种基于无人机的图像分析模型,使用残留学习和注意力机制来准确识别和定位大型光伏发电厂的光伏 (PV) 故障. 该模型通过提高故障检测效率来提高安全性,可靠性和生产率.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 大型光伏 (PV) 电站需要强大的故障识别,以确保安全和效率.
- 传统的光伏故障检测方法与现代装置的规模和复杂性作斗争.
- 自动化系统对于保持光伏发电的可靠性和生产率至关重要.
研究的目的:
- 开发一种自动化模型,用于在低压分布式光伏系统中准确识别和定位故障.
- 通过使用先进的深度学习技术,提高光伏图像检测的稳定性和准确性.
- 通过智能监控,提高大型光伏电站的安全性,可靠性和生产率.
主要方法:
- 基于残留学习的图像分析模型与注意力机制集成,用于无人机 (UAV) 图像数据.
- 该模型基于You Only Look Once v4 (YOLOv4) 网络,通过多尺度空间金字塔融合进行了优化.
- 整体IOU损失被纳入,以显著提高故障检测和定位的准确性和效率.
主要成果:
- 实验性比较表明,该模型在光伏阵列内的故障定位方面具有很高的准确性.
- 集成的残留学习和注意力机制提高了检测的稳定性和精度.
- 优化的YOLOv4网络有效地解决了复杂光伏系统中故障定位的挑战.
结论:
- 提出的基于无人机的图像分析模型对于低压分布式光伏系统的故障识别和定位是可行的和有效的.
- 该模型有助于提高大型光伏发电厂的安全性和可靠性维护.
- 这种方法为智能监控和管理光伏基础设施提供了一个有前途的解决方案.
相关概念视频
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01:24Bus Impedance Matrix
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01:18Fault Types
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01:21Three-Phase Short Circuit—Unloaded Synchronous Machine
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