低压交流串联故障电弧辨识方法

doi:10.19595/j.cnki.1000-6753.tces.220442

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Abstract Traditional protection switching devices can not open the line that occurs series arc fault in time, because current amplitude when line is series arc fault state is lower than it when line is normal state. Recently, the development of machine learning provides a new idea for the detection of arc fault. However, a large amount of data is needed to train an arc fault detection model, and it is expensive to make a circuit module that uses machine learning algorithm to recognise arc fault, which causes troubles for promotion and application of arc fault detection device (AFDD). To address these issues, this paper introduces an arc fault recognition method based on multi-feature fusion.
Firstly, a test device that simulates arc fault experiment is constructed, and series arc fault experiments are carried out under many load types, which accumulets a large number of test data. Secondly, the arcing processes of arc fault are observed within a smaller time scale with the help of high-speed camera, which helps to establish the relation between physical phenomenon of arcing process and waveform of arc current. Besides, the feature of circuit current waveform is analysed when line occurs arc fault. Thirdly, based on current zero feature and current ripple feature of circuit current, three characteristics, current average, harmonic amplitude sum, wavelet energy entropy, are extracted from time domain, frequency domain and signal disorder degree respectively. To avoid the influences of different load types on characteristics threshold selection, the characteristics ratio between arcing state and normal state serves as reference of characteristic threshold selection. Finally, the prototype of AFDD is made with the help of universal microcontroller (STM32), and the accuracy of arc fault detection method is verified by carring out experiments. Besides, the stability of the method is also verified with arc fault simulation experiments (arc generated by carbonization line) and special loads starting experiments.
The photos of arcing processes intuitively reflects current zero phenomenon and instability of arcing, and these physical phenomenon can be characterized with current zero feature and current ripple feature of the current waveform. Meanwhile, three characteristics can evidently distinguish arcing and normal state of line under different load types. In addition, the values of every characteristic is different under different load types, which causes difficulty for selecting threshold between normal and arcing state. However, the characteristic ratio exists similarity under different load types. Characteristic ratio can serve as reference of characteristic threshold selection. Through experimental verification, the accuracy of arc fault detection method is 90%, and applicability of the method is not affected by modes of arc fault simulation experiment.
The following conclusions can be drawn from experimental results: 1) Both current zero feature and current ripple feature of arcing processes can be used for identifying arcing state of line. 2) Three characteristics, current average, harmonic amplitude sum, wavelet energy entropy, are effective for distinguishing arcing and normal state of line under different load types. 3) The characteristic ratio of arcing and normal state can provide references for characteristics threshold selection, which avoids the effects of different load types on threshold selection.

Key words： Distribution system arc fault arcing characteristic detection method multi-feature fusion

Received: 28 March 2022

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TM501.2

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	Articles by authors
	He Zhipeng
	Li Weilin
	Deng Yunkun
	Zhao Hu

Cite this article:

He Zhipeng,Li Weilin,Deng Yunkun等. The Detection of Series AC Arc Fault in Low-voltage Distribution System[J]. Transactions of China Electrotechnical Society, 0, (): 3-3.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.220442 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/3

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