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 ›› 2020, Vol. 39 ›› Issue (2): 278-282.

• • 上一篇    下一篇

基于1D-CNN的串联故障电弧检测

周新城，吴自然，吴桂初   

  1. （温州大学 电气与电子工程学院，浙江 温州 325000)
  • 收稿日期:2019-09-25 出版日期:2020-02-15 发布日期:2020-02-15
  • 通讯作者: 吴自然（1984-），男，温州大学电气与电子工程学院副研究员。
  • 作者简介:周新城(1993-)，男，江西吉安人，温州大学电气与电子工程学院硕士研究生，主要从事电器智能化、故障检测方面的研究,浙江省温州市路鹿城区学院中路276号低压电器大楼，325000。
  • 基金资助:
    浙江省自然科学基金项目（LQ16E070004）；温州市科研项目（ZG2019017）

Series fault arc detection based on 1D-CNN

ZHOU Xin-cheng, WU Zi-ran, WU Gui-chu   

  1. (College of Electrical and Electronic Engineering, Wenzhou University, Zhejiang Wenzhou 325000, China)
  • Received:2019-09-25 Online:2020-02-15 Published:2020-02-15

摘要： 为提高串联故障电弧检测的可靠性，依据标准搭建串联故障电弧检测试验平台，设计数据实时采集装置采集了白炽灯、日光灯、空气压缩机、吹风机4种线性或者非线性负载在正常和故障情况下的电流数据共9 600组。提出利用一维卷积神经网络（1D-CNN）检测线路中电流信号对其分类，判断是否发生故障电弧。经测试该模型对各类负载的平均检测准确率达到100%，损失值在0.000 7以下。将模型导入嵌入式系统，准确度达到96.25%,证明设计的卷积神经网络架构可成功检测出串联故障电弧，降低火灾发生风险。

关键词: 故障电弧, 卷积神经网络, 火灾危险性

Abstract: In order to improve the reliability of series fault-arc detection, an experimental platform is built according to the standard, and a real-time data acquisition device is made to collect both faulty and non-faulty current data generated by 4 types of linear or non-linear loads, including incandescent lamps, fluorescent lamps, air compressors and blowers. A one-dimensional convolutional neural network (1D-CNN) is proposed to inspect current signals in the circuit and determine whether fault arcs occur. Testing results show that the average detection accuracy of the model for all kinds of loads reaches 100%, and the loss is below 0.0007. We also transplant the model into an embedded system, and the accuracy reaches 96.25%. It is proven that the designed convolutional neural network structure can successfully detect series fault arcs and reduce the risk of fire.

Key words:  fault arcs, convolutional neural networks, fire risk