Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals

Tho, Nguyen Thi Ngoc; Chakrabarty, Chandan Kumar; Yap, Keem Siah; Ghani, Aminuddin Ab

doi:10.1109/icos.2011.6079231

Cited by 1 publication

(2 citation statements)

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“…The PD patterns are mainly divided into three types: time resolved partial discharge (TRPD) [5,6], phase resolved partial discharge (PRPD) [7][8][9], and phase resolved pulse sequence (PRPS) [10]. Since TRPD is greatly affected by the signal propagation path and noise interference, it is not suitable for pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

“…However, few reports have recognized PD patterns directly based on 3D PRPS graphs due to the limitations of traditional recognition methods. The existing methods usually extract traditional low-dimensional, multiparameter features such as statistical features [12][13][14], wavelet coefficients [15][16][17], fractal features [18,19], Tamura texture features [20], and Weibull parameters [21], and then use classifiers such as support vector machine [22], neural networks [5,23], and clustering [19] to realize the recognition. One limitation is that the above methods almost exclusively apply to 2D graphs and have difficulty in recognizing 3D graphs.…”

Section: Introductionmentioning

confidence: 99%

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Partial Discharge Pattern Recognition Based on 3D Graphs of Phase Resolved Pulse Sequence

et al. 2020

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Partial discharge (PD) is an important phenomenon that reflects the insulation condition of electrical equipment. In order to protect the safety of power grids, it is of significance to diagnose the type of insulation defects inside the equipment accurately and early through PD pattern recognition. In this article, phase resolved pulse sequence (PRPS) graphs in 3D were constructed by the PD pulse data of the gas-insulated switchgear (GIS) acquired, then the histogram of oriented gradient (HOG) features were extracted directly from the 3D PRPS graphs, and finally the attribute selective Naïve Bayes classifier was used to recognize the discharge pattern. In addition, this method was compared with two traditional methods, i.e., the statistical method and the grayscale gradient co-occurrence matrix method, from three aspects. The result shows that 3D PRPS graphs have different morphology characteristics in vision under different defects, and the similarity among different voltages applied is higher than among different defects, so it is reasonable to use them as the basis for PD pattern recognition. The contrast indicates that the HOG method not only has the highest accuracy with the least requirement for pretreatment and training, but it also has robustness when the voltage applied changes. Consequently, this method has the universality for PD pattern recognition that is based on 3D PRPS graphs.

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