NDLI: Cross Hilbert-Huang transform based feature extraction method for multiple PQ disturbance classification

Content Provider	IEEE Xplore Digital Library
Author	Dalai, S. Dey, D. Chatterjee, B. Chakravorti, S. Bhattacharya, K.
Copyright Year	2013
Description	Author affiliation: Electr. Eng. Dept., Jadavpur Univ., Kolkata, India (Dalai, S.; Dey, D.; Chatterjee, B.; Chakravorti, S.; Bhattacharya, K.)
Abstract	This paper presents a new methodology of Cross-Hilbert Huang transform based feature selection for sensing simultaneous occurrence of multiple power quality disturbances. Kernel PCA is used for feature selection because this method is well suited for non-linear and non-stationary multiple power quality disturbances. A linear support vector machine is used for classification of the extracted features. Results show that the performance is comparable with the results reported in the literatures. The present method is generic in nature and can be applicable for topologically similar problems.
Starting Page	314
Ending Page	317
File Size	290515
Page Count	4
File Format	PDF
e-ISBN	9781479900831
DOI	10.1109/CATCON.2013.6737519
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Multiple Power Quality disturbance Power quality Transforms Feature extraction Eigenvalues and eigenfunctions Kernel Principal Component Analysis Support Vector Machine Cross Hilbert Huang Transform Kernel Principal component analysis
Content Type	Text
Resource Type	Article

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