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Frontiers of Mechanical Engineering

ISSN 2095-0233

ISSN 2095-0241(Online)

CN 11-5984/TH

Postal Subscription Code 80-975

2018 Impact Factor: 0.989

Front. Mech. Eng.    2008, Vol. 3 Issue (3) : 270-275    https://doi.org/10.1007/s11465-008-0042-1
PD pattern recognition based on multi-fractal dimension in GIS
ZHANG Xiaoxing, YAO Yao, TANG Ju, ZHOU Qian, XU Zhongrong
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University;
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Abstract This paper designs four types of gas insulated substation (GIS) defect models based on partial discharge (PD) characteristics and its defections. The GIS gray intensity images are constructed based on the mass specimens gathered by the ultra-high frequency and high-speed sampling systems. The multi-fractal dimension is founded on the box-counting dimension and multi-fractal theories. The GIS gray intensity images distillation methods, based on multi-fractal characteristics, is put forward. The box-counting dimension, multi-fractal dimension, and discharge centrobaric characteristics of the PD images are also extracted. The characteristic variables are then classified by the radial basis function (RBF) network. Identified results show that the methods can effectively elevate the discrimination of the four types of defects in GIS.
Issue Date: 05 September 2008
 Cite this article:   
ZHANG Xiaoxing,YAO Yao,TANG Ju, et al. PD pattern recognition based on multi-fractal dimension in GIS[J]. Front. Mech. Eng., 2008, 3(3): 270-275.
 URL:  
https://academic.hep.com.cn/fme/EN/10.1007/s11465-008-0042-1
https://academic.hep.com.cn/fme/EN/Y2008/V3/I3/270
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