Design and real time implementation of adaptive neural-fuzzy inference system controller based unity single phase power factor converter

Benyamina A., Moulahoum S., Colak I., BAYINDIR R.

ELECTRIC POWER SYSTEMS RESEARCH, vol.152, pp.357-366, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 152
  • Publication Date: 2017
  • Doi Number: 10.1016/j.epsr.2017.07.025
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.357-366
  • Keywords: Active power factor correction, Adaptive neural-fuzzy inference system, Artificial neural network, Real time, Fuzzy logic controller, VOLTAGE, FILTERS, ANFIS, PFC
  • Gazi University Affiliated: Yes


This paper presents a design and a real time application of an efficient adaptive neural-fuzzy inference system based voltage controller for a single-phase boost unity active power factor correction in order to improve its performances. Basically, the adaptive neural-fuzzy inference system is a combination of fuzzy logic and artificial neural network techniques. The proposed control improves the DC bus voltage loop and presents a good capacity to track the voltage reference point under a fast variation of the load with less fluctuation in the steady state. The adaptive neural-fuzzy inference system training datasets are extracted from the fuzzy logic controller model developed in MATLAB Simulink and its robustness has been verified experimentally under different measurement noises and disturbances. This technique presents good performances comparing with others approaches in terms of total harmonic distortion, power factor, the response time and the accuracy in the steady state under different parameters variation, non-linearity and the load change effect. (C) 2017 Elsevier B.V. All rights reserved.