  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.
