传输线路故障分类基于缩放波形波形图和CNN的组合,使用单侧传感器进行数据收集
Ahmed Sabri Altaie1, Mohamed Abderrahim1, Afaneen Anwer Alkhazraji2
1Department of System Engineering and Automation, University Carlos III of Madrid, Avada de la Universidad 30, 28911 Leganes, Madrid, Spain.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
波形变换与深度学习相结合,在分类电力传输故障方面实现了100%的准确性. 这种方法有效地分析了短暂故障特征,而不需要额外的算法.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 准确的故障分类对于电力传输网络的稳定性和可靠性至关重要.
- 传统的方法经常与故障信号的复杂性和短暂性质作斗争.
研究的目的:
- 开发使用波波变换和深度学习的电力传输网络高精度故障分类方法.
- 调查各种故障参数对分类准确性的影响.
主要方法:
- 利用相位电流和电压数据上的缩放连续波束转换 (S-CWT) 来创建 Skalogram 图像.
- 雇员预先训练有素的深度学习模型,使用这些刻度图像作为输入.
- 专注于选择最优的样本数量与CWT尺度相匹配.
主要成果:
- 在各种故障场景 (类型,位置,电阻值) 中实现了100%的分类准确性.
- 证明了特定的输入数据准备 (样本=尺度) 是高精度的关键.
- 在各种网络类型上验证了该方法的有效性.
结论:
- 波形变换是一种可靠的工具,用于捕捉具有优异时间频率分辨率的短暂故障特征.
- 拟议的波形深度学习方法为电力系统故障分类提供了强大的,高度准确的解决方案.
- 该方法的简单性和有效性消除了对复杂的补充算法的需求.
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