A Proposed Maximum Power Point Tracking by Using Adaptive Fuzzy Logic Controller for Photovoltaic Systems

Authors

1 Department of Electrical Engineering, Shahid Bahonar University of Kerman, Iran

2 Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, P.O. Box 76175133, Iran

3 Department of Electrical Engineering, Shahid Bahonar University of Kerman Iran

Abstract

This paper presents a proposed method to search maximum power point (MPP) based on the adaptive fuzzy logic control (AFLC) which is applied to photovoltaic (PV) systems under varying temperatures and radiations. The proposed system is composed of boost converter, two fuzzy controllers and load.  Whenever environmental conditions change in wide range, using only conventional fuzzy logic controller (CFLC) is not adequate and causes more errors in tracking. The proposed AFLC comprises two stages: Online and Offline tuning. The offline method by accurately setting CFLC controller parameters is applied for relatively stable atmospheric conditions. Meanwhile, the online method is considered for unstable atmospheric conditions and contains two fuzzy controllers-one primary, one secondary. The primary fuzzy controller is the CFLC, and the secondary controller is the decision-making, which due to atmospheric conditions, alters the primary fuzzy controller parameters in order to achieve a better answer compared to utilizing CFLC. Decision making controller with changing in irradiation and temperature changes gain of inputs of CFLC simultaneously that it increases rate and accuracy of tracking in comparison with using only fuzzy controller. By simulating results using CFLC and AFLC controllers, the proposed method is able to improved performance indicators with respect to CFLC.

Keywords

Scientia Iranica
Volume 23, Issue 3 - Serial Number 3
Transactions on Computer Science & Engineering and Electrical Engineering (D)
June 2016
Pages 1272-1281

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History

  • Receive Date: 06 October 2015
  • Revise Date: 21 December 2024
  • Accept Date: 27 July 2017

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