A coupling method for identifying arc faults based on short-observation-window SVDR

Jiang, Run and Bao, Guanghai and Hong, Qiteng and Booth, Campbell (2021) A coupling method for identifying arc faults based on short-observation-window SVDR. IEEE Transactions on Instrumentation and Measurement. ISSN 0018-9456

[thumbnail of Jiang-etal-IEEE-TOIM-2021-A-coupling-method-for-identifying-arc-faults-based]
Preview
 Text (Jiang-etal-IEEE-TOIM-2021-A-coupling-method-for-identifying-arc-faults-based)
Jiang_etal_IEEE_TOIM_2021_A_coupling_method_for_identifying_arc_faults_based.pdf
Accepted Author Manuscript
Download (7MB)| Preview

    Abstract

    This paper presents a new method for effective detection of AC series arc fault (SAF) and extraction of SAF characteristics in residential buildings, which addresses the challenges with conventional current detection methods in discriminating arcing and non-arcing current due to their similarity. Different from the traditional method, in the proposed method, the differential magnetic flux is coupled to obtain high-frequency signals by putting the live line and the neutral line through the current transformer, which can effectively solve the problem of SAF features disappearing in the trunk-line current. However, similar to the traditional method, the effectiveness of the proposed coupling method could also be compromised when being used in cases with dimmer load and load starting process. This is found to be caused by the presence of high-amplitude pulse phenomenon in the non-arcing signals in these scenarios, which are incorrectly detected as arcing signals in other loads. To address this issue, a short-observation-window singular value decomposition and reconstruction algorithm (SOW-SVDR) is used to enhance the capability to identify SAFs by the coupling method. The proposed method has been implemented and validated according to UL1699 standard with different types of loads connected to the system and also tested under their starting processes. The experimental results show that the proposed approach is more effective in detecting arc faults compared with existing methods.

    ORCID iDs

    Jiang, Run, Bao, Guanghai, Hong, Qiteng ORCID logoORCID: https://orcid.org/0000-0001-9122-1981 and Booth, Campbell ORCID logoORCID: https://orcid.org/0000-0003-3869-4477;
    • Item type:Article
      ID code:75930
      Dates:
      Date
      Event
      22 March 2021
      Published
      22 March 2021
      Published Online
      6 March 2021
      Accepted
      Notes:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
      Keywords:series arc fault, coupling signals, short observation- window singular value decomposition and reconstruction (SOW-SVDR), UL1699, Electrical engineering. Electronics Nuclear engineering, Instrumentation, Electrical and Electronic Engineering
      Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
      Department:Faculty of Engineering > Electronic and Electrical Engineering
      Depositing user: Pure Administrator
      Date deposited:24 Mar 2021 16:17
      Last modified:17 Apr 2021 00:47
      URI:https://strathprints.strath.ac.uk/id/eprint/75930
    CORE (COnnecting REpositories)