title: A Novel Arc Fault Detector for Early Detection of Electrical Fires.
abstract: Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD) is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires.
authors.affiliation: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China. yangknh@126.com.
authors.forename: Kai
authors.initials: K
authors.lastname: Yang
authors.affiliation: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China. phzzrc@hqu.edu.cn.
authors.forename: Rencheng
authors.initials: R
authors.lastname: Zhang
authors.affiliation: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China. yjhong@hqu.edu.cn.
authors.forename: Jianhong
authors.initials: J
authors.lastname: Yang
authors.affiliation: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China. xsjtan@sina.com.
authors.forename: Canhua
authors.initials: C
authors.lastname: Liu
authors.affiliation: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China. shouhong55@163.com.
authors.forename: Shouhong
authors.initials: S
authors.lastname: Chen
authors.affiliation: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China. zfj@fjut.edu.cn.|School of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China. zfj@fjut.edu.cn.
authors.forename: Fujiang
authors.initials: F
authors.lastname: Zhang
keywords: arc fault detector (AFD); cross talk; current variation; electrical fire; high-frequency energy; weighted least squares support vector machine