主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2026, Vol. 44 ›› Issue (1): 28-34.

• • 上一篇    下一篇

单相智能电能表数据表征模拟试验及与电气火灾关联分析

李树超1,2,3, 詹俏刚4, 王鑫1,2,3, 宗思璇5, 韩冲1,2,3   

  1. （1.应急管理部天津研究所，天津 300382； 2.工业与公共建筑火灾防控技术应急管理部重点实验室，天津 300381； 3.天津市消防安全技术重点实验室，天津 300381； 4.浙江省消防救援总队,浙江 杭州 310014； 5.西安科技大学 安全科学与工程学院，陕西 西安 710699）
  • 收稿日期:2025-06-03 修回日期:2025-06-11 出版日期:2026-01-15 发布日期:2026-01-15
  • 作者简介:李树超，应急管理部天津消防研究所副研究员，硕士，主要从事火灾视频分析、电子数据提取恢复等技术研究工作，天津市西青区富兴路2号，300382，lishuchao@tfri.com.cn。
  • 基金资助:
    天津市重点研发计划项目（23YFZCSN00250）；应急管理部天津消防研究所基科费项目（2025SJ22）

Simulation experiment on data characterization of single-phase smart meters and correlation analysis with electrical fires

Li Shuchao1,2,3, Zhan Qiaogang4, Wang Xin1,2,3, Zong Sixuan5, Han Chong1,2,3   

  1. (1. Tianjin Fire Science and Technology Research Institute of MEM, Tianjin 300382, China; 2. Key Laboratory of Fire Protection Technology for Industry and Public Building, Ministry of Emergency Management, Tianjin 300381, China; 3. Tianjin Key Laboratory of Fire Safety Technology, Tianjin 300381, China; 4.Zhejiang Fire and Rescue Brigade, Hangzhou Zhejiang 310014, China; 5. School of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an Shaanxi 710699, China)
  • Received:2025-06-03 Revised:2025-06-11 Online:2026-01-15 Published:2026-01-15

摘要： 智能电能表数据在电气火灾溯源中的技术价值日益凸显，但其记录的数据特征及与电气火灾的关联机制仍需深入探究。通过构建多场景故障模拟试验平台，结合智能电能表动态数据监测、示波器波形捕捉及红外热成像技术，系统分析了负载类型识别、单相供电电压波动及电流异常等场景下用电特征与火灾风险的关联规律。试验表明，不同负载类型下智能电能表功率因数与无功功率数据差异显著，可为火灾现场负载类型判别提供关键依据。电压异常波动下，过压导致阻性负载电流升高，欠压将引发恒功率负载电流补偿性增长，加剧导线过热风险。零火短路瞬间电流峰值可达260 A，释放能量超100 J，火线漏电（65 mA）1 min内漏点温度超300 ℃，过载（1.5倍）导线温升达104 ℃，劣质导线（0.5 mm）正常通流后温度达173.6 ℃，均存在引燃可燃物的直接风险。研究验证了智能电能表数据在火灾原因追溯中的技术有效性，提出强制安装漏保断路器、规范导线选型标准等主动防控策略，为电气火灾的精准预防、成因追溯及安全治理提供了理论支撑与实践参考。

关键词: 智能电能表, 异常用电数据, 电气火灾, 故障模拟

Abstract: The technical value of smart electricity meters data in tracing the causes of electrical fires has become increasingly prominent, yet the data characteristics and their linkage mechanisms with electrical fires still require further exploration. This study systematically analyzes the correlation between electrical characteristics and fire risks under scenarios such as load type identification, single-phase voltage fluctuations, and current anomalies through the construction of a multi-scenario fault simulation experimental platform. The platform integrates smart meter dynamic data monitoring, oscilloscope waveform capture, and infrared thermal imaging technology. Experimental results reveal substantial discrepancies in power factor and reactive power across load types, offering critical evidence for fire site load classification. In the condition of voltage deviations, overvol⁃tage elevates resistive load currents, while undervoltage triggers compensatory current surges in constant-power loads, intensifying conductor overheating. The instantaneous peak current of zero fire short circuit can reach 260 A, releasing more than 100 J of energy. The leakage point temperature of the live wire (65 mA) exceeds 300 ℃ within 1 min, and the temperature rise of the overloaded (1.5 times) wire reaches 104 ℃. The temperature of the inferior wire (0.5 mm2) after normal current flow reaches 173.6 ℃, all of which pose a direct risk of igniting combustibles. The research has verified the technical effectiveness of smart meter data in tracing the causes of fires, and proposed active prevention and control strategies such as mandatory installation of circuit breakers and standardized wire selection standards, providing theoretical support and practical reference for precise prevention, cause tracing, and safety management of electrical fires.

Key words: smart electricity meter, abnormal electricity consumption data, electrical fire, fault simulation