A Coupling Method for Identifying Arc Faults Based on Short-Observation-Window SVDR
A Coupling Method for Identifying Arc Faults Based on Short-Observation-Window SVDR
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DOI：
10.1109/tim.2021.3067660
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发表时间：
2021
影响因子：
5.6
通讯作者：
Run Jiang;Guanghai Bao;Q. Hong;C. Booth
中科院分区：
文献类型：
--
作者：
Run Jiang;Guanghai Bao;Q. Hong;C. Booth
本文提出了一种有效检测住宅交流串联电弧故障（SAF）并提取其特征的新方法，解决了传统电流检测方法因电弧和非电弧电流相似而难以区分的难题。与传统方法不同的是，本文提出的方法通过将火线和中性线通过电流互感器，将差磁通耦合得到高频信号，有效解决了主干线电流中SAF特征消失的问题。然而，与传统方法类似，在负载较弱和负载启动过程中，所提出的耦合方法的有效性也会受到影响。这是由于在这些情况下，非电弧信号中存在高幅度脉冲现象，而在其他负载中被错误地检测为电弧信号。为了解决这一问题，采用短观测窗奇异值分解与重构算法（SOW-SVDR），通过耦合方法增强对sar的识别能力。该方法已根据UL1699标准进行了实施和验证，并在不同类型的负载连接到系统中，并在其启动过程中进行了测试。实验结果表明，与现有方法相比，该方法能够更有效地检测AFs。
This article presents a new method for effective detection of ac series arc fault (AF) (SAF) and extraction of SAF characteristics in residential buildings, which addresses the challenges with conventional current detection methods in discriminating arcing and nonarcing 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 nonarcing 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 the 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 AFs compared with existing methods.