RESEARCH ARTICLE

Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances

  • Saeed VAFAEI ,
  • Alireza REZVANI ,
  • Majid GANDOMKAR ,
  • Maziar IZADBAKHSH
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  • Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran

Received date: 03 Sep 2014

Accepted date: 19 Dec 2014

Published date: 11 Sep 2015

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

Abstract

In recent years, many different techniques are applied in order to draw maximum power from photovoltaic (PV) modules for changing solar irradiance and temperature conditions. Generally, the output power generation of the PV system depends on the intermittent solar insolation, cell temperature, efficiency of the PV panel and its output voltage level. Consequently, it is essential to track the generated power of the PV system and utilize the collected solar energy optimally. The aim of this paper is to simulate and control a grid-connected PV source by using an adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) controller. The data are optimized by GA and then, these optimum values are used in network training. The simulation results indicate that the ANFIS-GA controller can meet the need of load easily with less fluctuation around the maximum power point (MPP) and can increase the convergence speed to achieve the MPP rather than the conventional method. Moreover, to control both line voltage and current, a grid side P/Q controller has been applied. A dynamic modeling, control and simulation study of the PV system is performed with the Matlab/Simulink program.

Cite this article

Saeed VAFAEI , Alireza REZVANI , Majid GANDOMKAR , Maziar IZADBAKHSH . Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances[J]. Frontiers in Energy, 2015 , 9(3) : 322 -334 . DOI: 10.1007/s11708-015-0362-x

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