Comparison OF Sensorless Current Controlled DC - DC Converter Using Intelligent Control Techniques

Authors

  • Hinduja S  Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Kariapatti, Tamil Nadu, India
  • Dr. Santhi  Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Kariapatti, Tamil Nadu, India

Keywords:

BOOST Converter, SEPIC Converter

Abstract

A sensor less voltage control mode (SVM) control is an observer method that provides the operating benefits of voltage mode control without voltage sensing. SVM has significant advantages over both conventional peak and average current mode control techniques in noise susceptibility and dynamic range. The elimination of current steady –state error and achieve high accuracy current estimation purpose using incentivization. It achieves the highest observation accuracy by compensating for all the known parasitic parameter. To control the voltage neural network is implemented. Considerably we have both positive and negative range deviations in power productions. To counter this in my project there would be trials of two converters used (Boost, SEPIC). It reduces the control complexity to a single loop. Also, simulation is done using MATLAB/SIMULINK model and results are presented to verify the operation.

References

  1. Qiao Zhang, Run Min, Qiaoling Tong, XuechengZou, “Sensorless Predictive Current Controlled DC–DC Converter with a Self-Correction Differential Current Observer,” IEEE Trans. Ind. Electron., VOL. 61, NO. 12, Dec 2014.
  2. C Nagarajan, M Muruganandam, D Ramasubramanian, “Analysis and Design of CLL Resonant Converter for Solar Panel-battery Systems”, International Journal of Intelligent Systems and Applications (IJISA), Volume 1, December 2012 pp 52-58.

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Published

2017-06-30

Issue

Section

Research Articles

How to Cite

[1]
Hinduja S, Dr. Santhi, " Comparison OF Sensorless Current Controlled DC - DC Converter Using Intelligent Control Techniques , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.71-74, May-June-2017.