DocumentCode
2368530
Title
Artificial Neural Network Based Implementation of Space Vector Modulation for Voltage Fed Inverter Induction Motor Drive
Author
Sadati, Nasser ; Barati, Farhad
Author_Institution
Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran
fYear
2006
fDate
6-10 Nov. 2006
Firstpage
4410
Lastpage
4414
Abstract
In this paper, a neural network based implementation of space vector modulation (SVM) of a two-level voltage fed inverter is proposed. This network has the advantage of very fast implementation of SVM algorithm, particularly when a dedicated application-specific IC chip is used instead of a digital signal processor (DSP). The proposed neural network consists of several subnets, a counter and a logic circuit. Subnets are used to implement the stages of SVM algorithm while the counter is used to apply the switching state vectors in their specified times to inverter. The logic circuit generates the inverter switches commands according to the outputs of the subnets. The scheme has been evaluated on an induction motor drive which results in an excellent performance. According to straight forward steps of the artificial neural network (ANN) design, the modulator can be easily extended to multi-level inverters
Keywords
application specific integrated circuits; digital signal processing chips; electric machine analysis computing; induction motor drives; invertors; neural nets; switching convertors; DSP; application-specific IC chip; artificial neural network; digital signal processor; logic circuits; multilevel inverters; space vector modulation; switching state vectors; voltage fed inverter induction motor drive; Application specific integrated circuits; Artificial neural networks; Counting circuits; Digital integrated circuits; Induction motor drives; Inverters; Logic circuits; Signal processing algorithms; Support vector machines; Voltage;
fLanguage
English
Publisher
ieee
Conference_Titel
IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on
Conference_Location
Paris
ISSN
1553-572X
Print_ISBN
1-4244-0390-1
Type
conf
DOI
10.1109/IECON.2006.347311
Filename
4153222
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