系列弧故障检测方法的研究 基于电压信号的家庭负载
Bin Li1, Jiahui Shu2, Feifan Cui1
1Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, 125105, China.
Scientific reports
|July 27, 2025
概括
本研究介绍了一种使用电压信号的先进串行弧故障检测方法. 这种新的方法将深度学习与注意力机制相结合,以提高识别电气故障的准确性.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 连续弧断在电气系统中构成重大火灾风险.
- 准确检测这些故障对于安全性和可靠性至关重要.
- 现有的检测方法可能缺乏足够的准确性或适应性.
研究的目的:
- 开发一种强大而准确的连续弧故障检测方法.
- 利用深度学习和注意力机制来增强特征提取.
- 验证拟议方法的性能和适应性.
主要方法:
- 建立了一个家庭实验平台来收集线路电压信号.
- 波形变换和主要组件分析 (PCA) 用于数据预处理和维度减少.
- 一个具有多尺度并行卷积的Inception模块被用于特征提取.
- 双向长期短期记忆 (BiLSTM) 网络与自我注意机制相结合,用于序列建模和特征增强.
- 最终的分类是使用一个完全连接的层进行的.
主要成果:
- 与现有的算法相比,拟议的方法在识别连续弧故障方面表现出优异的性能.
- 在不同采样频率的实验证实了该方法的良好适应性.
- 在采样频率为10kHz时,获得了最佳的识别准确度.
结论:
- 开发的基于电压信号的方法为连续弧故障检测提供了非常有效的解决方案.
- 整合Inception,BiLSTM和注意力机制显著提高了检测准确度.
- 这些发现为开发下一代串联电弧故障检测装置提供了宝贵的理论指导.
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