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;