NDLI: Control of switching angles for a CMLI using ANN

Content Provider	IEEE Xplore Digital Library
Author	Kumar, J. Gambhir, J. Kumar, A.
Copyright Year	2014
Description	Author affiliation: PEC Univ. of Technol., Chandigarh, India (Kumar, J.; Gambhir, J.; Kumar, A.)
Abstract	The field of neural networks has a history of some five decades but has found solid applications only in the past fifteen years, and the field is still developing rapidly. Thus, it is distinctly different from the fields of control systems or optimization where the terminology, basic mathematics, and design procedures have been firmly established and applied for many years. In this work, a neural network fitting tool has been used. Neural network fitting tool helps us to select data, create and train a network, and evaluate its performance using mean square error and regression analysis. Then trained neural network is used for supplying switching angles for cascaded multi-level inverter. Thus, different switching angles corresponding to a particular value of modulation index (m) are fed to CMLI by trained Neural Network. Simulated line to line output voltage and its harmonic spectrum has been calculated and compared with analytically obtained values of total harmonic distortion. Analytic results are obtained for the switching angles computed using optimization based method for elimination of lower order harmonics in MATLAB 7.10.0.
Sponsorship	IEEE Chandigarh Subsect.
Starting Page	1
Ending Page	6
File Size	325857
Page Count	6
File Format	PDF
e-ISBN	9781479922918
DOI	10.1109/RAECS.2014.6799596
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Cascaded multilevel inverter Modulation index Modulation Switches Artificial neural networks Trained neural network Harmonic analysis Artificial neural network Selective harmonic elimination Biological neural networks
Content Type	Text
Resource Type	Article

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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
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