智能电表的数据驱动松散接触诊断方法
Wenpeng Luan1, Yajuan Huang1, Bochao Zhao1
1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究介绍了一个基于数据的框架,使用局部异常因子 (LOF) 和多重线性回归 (MLR) 早期检测智能电表终端故障,防止火灾危险.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 预测性维护是指预测性维护.
背景情况:
- 智能电表中松散的螺丝端子会导致过热和弧形,导致火灾风险.
- 早期故障检测对于减轻这些危险并确保电网安全至关重要.
研究的目的:
- 提出一个新的数据驱动框架,用于智能计量器终端的早期故障检测.
- 准确诊断终端接触退化和相关的火灾危险.
主要方法:
- 从模拟的智能电表数据中利用电压差异.
- 集成局部异常因子 (LOF) 用于电弧故障检测 (异常因子识别).
- 用多重线性回归 (MLR) 来量化松散接触 (阻力分析).
主要成果:
- 该LOF-MLR框架成功地识别了弧形故障,并量化了松散接触电阻.
- 与传统方法相比,证明了更高的诊断准确性和适应性.
- 在各种负载条件下经过验证的坚固性.
结论:
- 拟议的数据驱动框架有效检测智能电表终端故障,提高安全性.
- LOF和MLR集成为智能计量基础设施的预测性维护提供了可靠的解决方案.
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