跨领域的零射击学习用于增强高压断路器故障诊断
Qiuyu Yang1, Yuxiang Liao1, Jianxing Li1
1School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, 350118, PR China.
概括
这项研究引入了一种新的方法,用于在不需要标记数据的情况下诊断高压断路器 (HVCB) 的故障. 该方法通过准确识别不同HVCB类型的问题来提高电网稳定性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 高压断路器 (HVCB) 对于稳定的电力供应至关重要.
- 目前的故障诊断方法需要大量的标记数据,并且在HVCB模型中缺乏概括性.
- 挑战包括独特的操作环境和HVCBs复杂的机械结构.
研究的目的:
- 为HVCBs开发一种可通用的故障诊断方法,克服现有方法的局限性.
- 为了实现准确的故障诊断,而不依赖于每个HVCB类型的大型标记数据集.
- 通过先进的诊断来提高电力系统的可靠性和稳定性.
主要方法:
- 提出了一种新的跨领域零射击学习 (CDZSL) 方法用于HVCB故障诊断.
- 实施了结合振动和电流信号的自适应加权聚变策略.
- 开发了一种自动语义构造方法来绕过手动故障语义.
- 设计了一个多通道残余卷积神经网络,用于深度特征提取.
- 利用本地子空间嵌入来实现有效的语义特征对齐.
主要成果:
- 该CDZSL方法在诊断各种HVCB类型的故障方面表现出卓越的性能.
- 适应加权聚变战略有效地集成了多模式传感器数据.
- 自动语义构造促进了更广泛的应用.
- 深度学习模型成功地提取了强大的低级特征,用于跨领域诊断.
结论:
- 拟议的CDZSL方法为HVCB故障诊断提供了一个强大的和可通用的解决方案.
- 这种方法显著减少了对标记数据集的依赖,使其更实用.
- 这些发现有助于提高电力基础设施的稳定性和可靠性.
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