Detection of high impedance faults using current transformers for sensing and identification based on features extracted using wavelet transform. Issue 12 (1st September 2016)

• Record Type: Journal Article
• Authors: Chen, Jichao; Phung, Toan; Blackburn, Trevor; Ambikairajah, Eliathamby; Zhang, Daming
• Is Part Of: IET generation, transmission & distribution. Volume 10:Issue 12(2016)
• Page Start: 2990
• Page End: 2998
• Publication Date: 2016-09-01
• Subjects: electric sensing devices -- wavelet transforms -- fault currents -- current transformers; high impedance fault detection -- current transformers -- wavelet transform -- feature extraction -- network protection -- HIF arcing currents -- contact surface -- fault currents -- WT coefficients -- transformer magnetising inrush currents -- capacitor switching transients -- transient disturbances -- distribution network -- nonfault transient events -- Daubechies wavelet Db4 -- detection rates -- discrimination rates; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2016.0021
• Languages: English
• ISSNs: 1751-8687

Abstract

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.