负载端电弧故障电压检测与形态小波辨识

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Abstract The existing fault detection and identification methods for series arc current are based on the feature extraction of signal singularities for arc current with zero moment, mutation rate of climb and harmonic component, etcval However, there is an erroneous judgement which is caused by the current signal singularities of nonlinear load in rated duration, inductive load in startup process, etc. in distribution line. According to the principle of load side voltage not to be impacted by the current signal singularities in normal operation for distribution circuit, a novel arc fault detection and identification method for load side voltage is put forward, which can not only obtain series arc fault feature, but also avoid the defection of existing arc fault current method. By arc fault identification decision-making functions modeling with morphological wavelet, the combination identification model of morphology open-filter with the fourth dimension wavelet transform is selected to analyze arc fault experiment detection signal of six kinds load side voltage. Finally, the fourth dimension wavelet component criterion thresholds of arc fault signal for load side voltage are provided, that are ten times the normal condition for kinds of load.

Key words： Distributing line series arc arc voltage morphology wavelet transforms fault identification

Received: 09 June 2013 Published: 17 June 2014

PACS:

TM501.2

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	Articles by authors
	Miao Xiren
	Guo Yinting
	Tang Jincheng
	Zhang Liping

Cite this article:

Miao Xiren,Guo Yinting,Tang Jincheng等. Load Side Arc Fault Voltage Detection and Identification with Morphological Wavelet[J]. Transactions of China Electrotechnical Society, 2014, 29(3): 237-244.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I3/237

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