PD pattern recognition based on multi-fractal dimension in GIS |
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Authors: | Xiaoxing ZHANG Yao YAO Ju TANG Qian ZHOU Zhongrong XU |
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Affiliation: | (1) State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, 400044, China |
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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. TheGIS 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.
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Translated from Chinese Journal of Scientific Instrument, 2007, 28(4): 597–601 译自: 仪器仪表学报] |
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Keywords: | GIS PD box-counting dimension multifractal pattern recognition |
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