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Fault Detection and Diagnosition System for a 19 Level Cascaded Multilevel Inverter using ANN
Authors(4) :-R. Ramkumar, R. Harine, G. Akthar Shehanaz, S. Gowthami
An inverter is a circuit used for converting direct current to alternating current. They are used in a wide range of applications, from small power supply for a computer to large industrial applications for transporting bulk power. Multilevel inverters as compared to single level inverters have advantages like minimum harmonic distortion and it can operate on several voltage levels. The paper aims at the detection and diagnosition of the various faults occurring in a single phase nineteen level inverter based on Artificial Neural Networks. The role of Artificial Neural Network in fault diagnosition is studied and simulated using MATLAB/SIMULINK. The single phase nineteen level inverter system is designed and their characteristics are observed. The datas like voltage, current and the error values of an inverter is collected by varying the load conditions. The neural network is trained to detect the faults with the help of the data sets and tested in order to eliminate the faults occurring in an inverter. Thus, by utilizing the proposed neural network fault diagnosis system, a better understanding about fault behaviors, diagnostics, and detections of a multilevel inverter system can be accomplished.
R. Ramkumar, R. Harine, G. Akthar Shehanaz, S. Gowthami
Back propagation algorithm, Fault diagnosis, Multilevel inverter, Neural network, Power electronics.
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Published in : Volume 3 | Issue 7 | September-October 2017
Date of Publication : 2017-10-31
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 153-159
Manuscript Number : IJSRST173729
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
R. Ramkumar, R. Harine, G. Akthar Shehanaz, S. Gowthami, "Fault Detection and Diagnosition System for a 19 Level Cascaded Multilevel Inverter using ANN", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 7, pp.153-159, September-October-2017.
Journal URL : http://ijsrst.com/IJSRST173729