Feature extraction and recognition of UHF partial discharge signals in GIS based on dual-tree complex wavelet transform

2009 ◽  
pp. n/a-n/a ◽  
Author(s):  
Yanbin Xie ◽  
Ju Tang ◽  
Qian Zhou
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Partial Discharge ◽  
Complex Wavelet Transform ◽  
Complex Wavelet

scholarly journals Pattern Recognition of Partial Discharge by Using Scale parameters-Energy Entropy Characteristic Pairs

2019 ◽  
Vol 136 ◽  
pp. 01026
Author(s):  
Liu Dongchao ◽  
Xiong Hui ◽  
Zhu Xiaotong ◽  
Xu Lei
Keyword(s):  
Wavelet Transform ◽  
Partial Discharge ◽  
Structure Characteristic ◽  
Complex Wavelet Transform ◽  
Scale Parameters ◽  
Energy Entropy ◽  
Feature Information ◽  
Complex Wavelet ◽  
Feature Dimension ◽  
Entropy Characteristic

In this paper, the complex wavelet transform (CWT) was used to process the ultra-high frequency partial discharge (UHF PD) signal in gas insulated switchgear (GIS) at different scales. The trend curves of complex wavelet transform energy entropy (CWT-EE) under different decomposition scale were analyzed, and it was found that the PD feature information mainly distributed in the scales, in which the gradient of CWT-EE is big. Besides, The CWT-EE characteristics and their scales were extracted to the structure characteristic pairs for PD type identification. The recognition results show that the characteristic pair could effectively identify four typical defects in GIS and obviously reduce the feature dimension.

CONTOUR-BASED FEATURE EXTRACTION USING DUAL-TREE COMPLEX WAVELETS

2007 ◽  
Vol 21 (07) ◽  
pp. 1233-1245 ◽  
Author(s):  
G. Y. CHEN ◽  
W. F. XIE
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Invariant Property ◽  
Complex Wavelet Transform ◽  
Translation Invariant ◽  
Feature Extraction Method ◽  
Complete Representation ◽  
Complex Wavelet ◽  
The Fourier Transform

A contour-based feature extraction method is proposed by using the dual-tree complex wavelet transform and the Fourier transform. Features are extracted from the 1D signals r and θ, and hence the processing memory and time are reduced. The approximate shift-invariant property of the dual-tree complex wavelet transform and the Fourier transform guarantee that this method is invariant to translation, rotation and scaling. The method is used to recognize aircrafts from different rotation angles and scaling factors. Experimental results show that it achieves better recognition rates than that which uses only the Fourier features and Granlund's method. Its success is due to the desirable shift invariant property of the dual-tree complex wavelet transform, the translation invariant property of the Fourier spectrum, and our new complete representation of the outer contour of the pattern.

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