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Improved control configuration of PWM rectifiers based on neuro-fuzzy controller

Overview of attention for article published in SpringerPlus, July 2016
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Title
Improved control configuration of PWM rectifiers based on neuro-fuzzy controller
Published in
SpringerPlus, July 2016
DOI 10.1186/s40064-016-2781-5
Pubmed ID
Authors

Hakan Acikgoz, O. Fatih Kececioglu, Ahmet Gani, Ceyhun Yildiz, Mustafa Sekkeli

Abstract

It is well-known that rectifiers are used widely in many applications required AC/DC transformation. With technological advances, many studies are performed for AC/DC converters and many control methods are proposed in order to improve the performance of these rectifiers in recent years. Pulse width modulation (PWM) based rectifiers are one of the most popular rectifier types. PWM rectifiers have lower input current harmonics and higher power factor compared to classical diode and thyristor rectifiers. In this study, neuro-fuzzy controller (NFC) which has robust, nonlinear structure and do not require the mathematical model of the system to be controlled has been proposed for PWM rectifiers. Three NFCs are used in control scheme of proposed PWM rectifier in order to control the dq-axis currents and DC voltage of PWM rectifier. Moreover, simulation studies are carried out to demonstrate the performance of the proposed control scheme at MATLAB/Simulink environment in terms of rise time, settling time, overshoot, power factor, total harmonic distortion and power quality.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 19%
Other 2 13%
Lecturer 2 13%
Professor > Associate Professor 2 13%
Student > Doctoral Student 1 6%
Other 1 6%
Unknown 5 31%
Readers by discipline Count As %
Engineering 7 44%
Unspecified 1 6%
Energy 1 6%
Agricultural and Biological Sciences 1 6%
Unknown 6 38%