基于CNN转换器并行神经网络与值移动优化的低压弧断的研究
Xin Ning1,2, Tianli Ding3, Hongwei Zhu3
1State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
这项研究引入了用于低压电弧故障检测的并行神经网络,达到99.74%的准确性. 这种先进的电弧故障断路器 (AFCI) 技术通过可靠地识别电气故障来提高安全性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 安全系统 安全系统
背景情况:
- 低压电弧断层带来了严重的火灾和冲击风险.
- 有效的电弧故障检测对于防止事故和保护财产至关重要.
- 现有的方法可能在弧度故障期间与不同的负载特征作斗争.
研究的目的:
- 开发一种高效,准确的低压电弧故障识别方法.
- 为了利用深度学习来增强弧线故障检测.
- 提高住宅和工业环境中电气系统的安全性.
主要方法:
- 一个平行神经网络架构,结合了卷积神经网络 (CNN) 和变压器.
- CNNs处理低频电流组件; 变压器处理高频组件.
- 软max分类用于连接特征的监督学习.
主要成果:
- 实现了99.74%的高检测精度.
- 使用值移动方法,证明了错误判断率的降低.
- 由于其精简的结构,拟议的网络显示了识别效率的提高.
结论:
- 平行神经网络有效地检测低压弧断.
- 混合方法结合了CNN和变压器的优势,实现了强大的识别.
- 这种方法为增强电气安全系统提供了一个有前途的解决方案.
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