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
Link To Document :
بازگشت