Detection of high impedance faults using current transformers for sensing and identification based on features extracted using wavelet transform

Abstract

High impedance fault (HIF) has long been a challenging problem in network protection due to its random behaviour and low magnitude. The conventional protection devices cannot be utilised since the HIF does not draw enough current to cause tripping. The feature of wavelet transform (WT) which decomposes a signal into different frequency bands and locations in time can be utilised to extract HIF features and detect its occurrence. In the study, HIF arcing currents associated with different types of contact surface are produced by experiments set up in the laboratory. Features of the fault currents are extracted and a new detection criterion is developed based on WT coefficients. Transformer magnetising inrush currents and capacitor switching transients are also produced by experiment and simulation, respectively, to represent transient disturbances commonly occurred in the distribution network. The detection criterion is used to discriminate the HIF from other normal non-fault transient events. Three different types of wavelet are tested and the Daubechies wavelet Db4 gives the best performance based on its detection and discrimination rates. The efficacy of utilising existing conventional current transformers for HIF application is also tested and verified by experiment.