Adaptive fuzzy controller based MPPT for photovoltaic systems

Abstract This paper presents an intelligent approach to optimize the performances of photovoltaic systems. The system consists of a PV panel, a DC–DC boost converter, a maximum power point tracker controller and a resistive load. The key idea of the proposed approach is the use of a fuzzy controller with an adaptive gain as a maximum power point tracker. The proposed controller integrates two different rule bases. The first is used to adjust the duty cycle of the boost converter as in the case of a conventional fuzzy controller while the second rule base is designed for an online adjusting of the controller’s gain. The performances of the adaptive fuzzy controller are compared with those obtained using a conventional fuzzy controllers with different gains and in each case, the proposed controller outperforms its conventional counterpart.

[1]  P. Sirisuk,et al.  Implementation of maximum power point tracking using fuzzy logic controller for solar-powered light-flasher applications , 2004, The 2004 47th Midwest Symposium on Circuits and Systems, 2004. MWSCAS '04..

[2]  Ali Belmehdi,et al.  Optimization of fuzzy controllers by neural networks and hierarchical genetic algorithms , 2007, 2007 European Control Conference (ECC).

[3]  Weidong Xiao,et al.  A modified adaptive hill climbing MPPT method for photovoltaic power systems , 2004, 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551).

[4]  Aissa Chouder,et al.  Simulation of fuzzy-based MPP tracker and performance comparison with perturb & observe method , 2008 .

[5]  Y.S. Boutalis,et al.  New maximum power point tracker for PV arrays using fuzzy controller in close cooperation with fuzzy cognitive networks , 2006, IEEE Transactions on Energy Conversion.

[6]  Slimane Hadji,et al.  Development of an algorithm of maximum power point tracking for photovoltaic systems using genetic algorithms , 2011, International Workshop on Systems, Signal Processing and their Applications, WOSSPA.

[7]  Chiung-Chou Liao,et al.  Genetic k-means algorithm based RBF network for photovoltaic MPP prediction , 2010 .

[8]  Djamila Rekioua,et al.  Fuzzy logic control of stand-alone photovoltaic system with battery storage , 2009 .

[9]  Rached Dhaouadi,et al.  Efficiency Optimization of a DSP-Based Standalone PV System Using Fuzzy Logic and Dual-MPPT Control , 2012, IEEE Transactions on Industrial Informatics.

[10]  V. Quaschning,et al.  Numerical simulation of current-voltage characteristics of photovoltaic systems with shaded solar cells , 1996 .

[11]  A. Messai,et al.  FPGA-based implementation of a fuzzy controller (MPPT) for photovoltaic module , 2011 .

[12]  C. Larbes,et al.  Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system , 2009 .

[13]  Weidong Xiao,et al.  [IEEE 2004 IEEE 35th Annual Power Electronics Specialists Conference - Aachen, Germany (20-25 June 2004)] 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551) - A novel modeling method for photovoltaic cells , 2004 .

[14]  Jiann-Fuh Chen,et al.  Novel maximum-power-point-tracking controller for photovoltaic energy conversion system , 2001, IEEE Trans. Ind. Electron..

[15]  M. Vitelli,et al.  Optimization of perturb and observe maximum power point tracking method , 2005, IEEE Transactions on Power Electronics.

[16]  Soteris A. Kalogirou,et al.  Maximum power point tracking using a GA optimized fuzzy logic controller and its FPGA implementation , 2011 .