NDLI: Support Vector Machine for power quality disturbances classification using higher-order statistical features

Content Provider	IEEE Xplore Digital Library
Author	Palomares-Salas, J.C. Aguera-Perez, A. de la Rosa, J.J.G.
Copyright Year	2011
Description	Author affiliation: Research Group PAIDI-TIC-168: Computational Instrumentation and Industrial Electronics (ICEI) (Palomares-Salas, J.C.; Aguera-Perez, A.; de la Rosa, J.J.G.)
Abstract	Support Vector Machine (SVM), which is based on Statistical Learning theory, is a universal machine learning method. This paper proposes the application of SVM in classifying to several power quality disturbances. For this purpose, a process based in HOS has been realized to extract features that help in classification. In this stage the geometrical pattern established via higher-order statistical measurements is obtained, and this pattern is function of the amplitudes and frequencies of the power quality disturbances associated to the 50-Hz power-line. Once the features are managed will be segmented to form training and test sets and them will be applied in the statistical method used to perform automatic classification of PQ disturbances. The result is shown according to correlation and mistake rates.
Starting Page	6
Ending Page	10
File Size	481589
Page Count	5
File Format	PDF
ISBN	9781424488063
e-ISBN	9781424488070
e-ISBN	9781424488056
DOI	10.1109/CPE.2011.5942198
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-01
Publisher Place	Estonia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Correlation Power quality Feature extraction Kernel Monitoring
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Pedestrian detection using hybrid statistical feature

Online power quality disturbances identification using incremental wavelet decomposition and support vector machine

Application of Feature Extraction and clustering in mammogram classification using Support Vector Machine

Feature selection and accurate classification of single and multiple power quality events

Feature selection and accurate classification of power quality disturbances

Statistical block-based DWT features for digital mammograms classification

Kernel principal component analysis for power quality problem classification

Automated system for drilling operations classification using statistical features

Features Discovery for Web Classification Using Support Vector Machine

Support Vector Machine for power quality disturbances classification using higher-order statistical features

Similar Documents

Pedestrian detection using hybrid statistical feature

Online power quality disturbances identification using incremental wavelet decomposition and support vector machine

Application of Feature Extraction and clustering in mammogram classification using Support Vector Machine

Feature selection and accurate classification of single and multiple power quality events

Feature selection and accurate classification of power quality disturbances

Statistical block-based DWT features for digital mammograms classification

Kernel principal component analysis for power quality problem classification

Automated system for drilling operations classification using statistical features

Features Discovery for Web Classification Using Support Vector Machine

Support Vector Machine for power quality disturbances classification using higher-order statistical features