智能电网的智能岛屿检测框架使用波形电梯图和HOG功能融合
Kumaresh Pal1, Kumari Namrata1, Ashok Kumar Akella1
1Department of Electrical Engineering, National Institute of Technology, Jamshedpur, 831014, India.
Scientific reports
|August 19, 2025
概括
一种新的机器学习方法有效地检测到电网中的意外岛屿,即使在困难的条件下. 这种先进的岛屿检测方案通过克服传统方法的局限性来提高电网稳定性和安全性.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 机器学习 机器学习
背景情况:
- 由于分布式发电 (DG) 的增加,在电力分配网络中意外的岛屿构成重大风险.
- 传统的岛屿检测方案 (IDS) 在平衡的负载生成条件下扎,导致非检测区 (NDZ).
研究的目的:
- 开发一种基于机器学习的新,可靠和强大的岛屿检测方案.
- 解决现有IDS的局限性,特别是在NDZ内.
主要方法:
- 使用连续波形变换 (CWT) 来从电压和电流的总波扭曲 (THD) 信号生成 Skalogram 图像.
- 从图图像中提取定向梯度 (HOG) 图形图表的特征,以捕获岛屿签名.
- 使用随机森林分类器进行强大的检测和最小的参数调整.
主要成果:
- 拟议的HOG基于特征的机器学习方法在准确性,精度,回忆和F1分数方面明显优于最先进的方法.
- 在各种噪音条件和具有挑战性的场景中表现出高可靠性和稳定性.
- 在OPAL-RT平台上的实时测试证实了其实际适用性和系统的稳定性.
结论:
- 这项研究引入了一种非常有效的解决方案,用于检测意外的岛屿,提高电网的稳定性和安全性.
- 新的机器学习方法比传统的岛屿检测方案提供了显著的进步.
- 该系统为具有高DG透率的当代电力配电网络提供了实际可靠性.
关键词:
连续波形变换连续波形变换.导向梯度梯度的直方图.岛屿检测检测 岛屿检测检测机器学习 机器学习随机森林分类器是随机森林分类器.标尺图像图像 标尺图像图像智能电网的稳定性 智能电网的稳定性总的波扭曲 总的波扭曲更多相关视频
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