Detecting the High impedance fault (HIF) in distribution systems plays an important role in power utilization safety. However, many HIFs are challenging to be identified due to their low currents and diverse characteristics. In particular, the slight nonlinearity during weak arcing processes, the distortion offset caused by the lag of heat dissipations, and the interference of background noises could lead to invalid of traditional detection algorithms. This paper proposes a distortion-based algorithm to improve the reliability of HIF detection. Firstly, the challenges brought by the diversity of HIF characteristics are illustrated with the experiments in a 10kV distribution system. Then, HIFs are classified into five types according to their characteristics. Secondly, an interval slope is defined to describe the waveform distortions of HIFs, and is extracted with the methods of the linear least square filtering (LLSF) and the Grubbs-criterion-based robust local regression smoothing (Grubbs-RLRS), so that feature descriptions under different fault conditions can be unified. Thirdly, an algorithm, as well as the criteria, is proposed to identify the fault features described by the interval slopes. Finally, the detection reliability of the algorithm is thoroughly verified with field HIF data, and the results show the improvements by comparing it to other ad-vanced algorithms.
Data-Driven Linearization based Arc Fault Prediction in Medium Voltage Electrical Distribution System
ABB Ability Innovation Center（ABB能力创新中心）；Indian Institute of Technology, Tirupati（蒂鲁帕蒂印度理工学院）；BITS Pilani Hyderabad Campus（比拉尼理工学院海德拉巴校区）；Indian Institute of Science Education and Research, Kolkata（加尔各答科学教育研究印度研究所）
