NDLI: A Fast and Universal Neuro-Based SVM Algorithm for Multi-Level Converters

Content Provider	IEEE Xplore Digital Library
Author	Saeedifard, M. Rad, H. Saligheh Bakhshai, A. Iravani, R.
Copyright Year	2007
Description	Author affiliation: Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, K7L 3N6, Canada. email: alireza.bakhshai@queensu.ca (Bakhshai, A.) \|\| Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA. email: hamid@deas.harvard.edu (Rad, H. Saligheh) \|\| Center for Applied Power Electronics (CAPE), Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada. email: mary.saeedifard@utoronto.ca (Saeedifard, M.) \|\| Center for Applied Power Electronics (CAPE), Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada. email: iravani@ecf.utoronto.ca (Iravani, R.)
Abstract	This paper proposes a novel, simple and fast classification algorithm for implementation of Space Vector Modulation (SVM) method for a Diode Clamped Multi-level Converter (DCMC) with an arbitrary number of levels. The proposed algorithm is based on a classifier neural network which provides a straightforward and computationally efficient approach without the use of trigonometric calculations or look-up tables to identify (i) the location of reference voltage vector, (ii) its adjacent switching voltage vectors, and (iii) their corresponding on-duration time intervals. The feasibility of the proposed SVM algorithm is validated based on theoretical analysis, simulation studies and experimental tests on a DSP-controlled, 5 kVA, three-level converter system.
Starting Page	1508
Ending Page	1514
File Size	6480297
Page Count	7
File Format	PDF
ISBN	1424407133
ISSN	10482334
DOI	10.1109/APEX.2007.357717
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-02-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Voltage Classification algorithms Diodes Neural networks Computer networks Algorithm design and analysis Computational modeling Analytical models Classification Technique Space Vector Modulation Multi-Level Converters Competitive Neural Network
Content Type	Text
Resource Type	Article

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