基于分形维数和支持向量机的串联电弧故障诊断方法

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Abstract Arc fault is one of the important reasons for electrical fires. Due to the diagnostic difficulties of randomness, diversity and concealment in series arc faults, a new diagnostic method for series arc fault is designed in this paper to raise the fault diagnostic rate. Series arc fault currents were acquired by high frequency current transformers and a high-speed data acquisition system. To extract characteristic information of series arc faults, the chaotic characteristics of high frequency currents were quantitatively described by fractal dimensions. Afterwards, the featured vectors of series arc fault were constructed by the box dimension and the correlation dimension. And current characteristic vectors were classified by least squares support vector machine (LSSVM). Then, the normal state and the series arc fault state were correctly discriminated. Finally, the designed method was verified via the set-up experimental platform. The diagnostic rate of series arc fault is over 98.0%, which shows the designed method has good generalization ability.

Key words： Series arc fault fractal dimension high frequency signal box dimension correlation dimension support vector machine

Received: 07 August 2014 Published: 03 March 2016

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TM501

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	Articles by authors
	Yang Kai
	Zhang Rencheng
	Yang Jianhong
	Du Jianhua
	Chen Shouhong
	Tu Ran

Cite this article:

Yang Kai,Zhang Rencheng,Yang Jianhong等. Series Arc Fault Diagnostic Method Based on Fractal Dimension and Support Vector Machine[J]. Transactions of China Electrotechnical Society, 2016, 31(2): 70-77.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I2/70

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