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

Run Jiang, Guanghai Bao, Qiteng Hong, Campbell Booth

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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.
Original languageEnglish
Number of pages9
JournalIEEE Transactions on Instrumentation and Measurement
Early online date22 Mar 2021
Publication statusE-pub ahead of print - 22 Mar 2021


  • series arc fault
  • coupling signals
  • short observation- window singular value decomposition and reconstruction (SOW-SVDR)
  • UL1699

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