Partial Discharge Pattern Classification by Singular Value Decomposition of Wavelet Packet Energy Features[J]. 2017, 51(8): 116-121.
DOI:
Partial Discharge Pattern Classification by Singular Value Decomposition of Wavelet Packet Energy Features[J]. 2017, 51(8): 116-121.DOI: 10.7652/xjtuxb201708019.
Partial Discharge Pattern Classification by Singular Value Decomposition of Wavelet Packet Energy Features
To extract the effective partial discharge(PD)characteristic parameters from a large amount of redundant frequency information
an energy feature extraction scheme combining wavelet packet decomposition(WPD)with singular value decomposition(SVD)is proposed. A set of energy features is selected from vast energy information to represent the energy of each WPD node
by which PD pattern can be classified. The EM signals from four PD patterns are decomposed by WPD and the energy for each WPD node is calculated
then SVD is introduced to select the energy features among the whole WPD tree
and classification model based on these features is used to identify PD pattern. It is shown that SVD of the wavelet packet energy features selected from the information with full of invalid and redundant signals is an effective way to classify partial discharge patterns.
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