Using ANFIS, PSO, FCN in Cooperation with Fuzzy Controller for MPPT of Photovoltaic Arrays

In this paper, an on-line method for maximum power point tracker using fuzzy set theory is presented to improve energy conversion efficiency. This method is proposed, by using a fuzzy cognitive network, which is in close cooperation with the presented fuzzy controller. The proposed approach is based on the combination of Particle Swarm Optimization (PSO) and Adaptive-Network- based Fuzzy Inference Systems (ANFIS) methods, and it is used for the determination of proper weight matrices that lead the Fuzzy Cognitive Map to desired steady states. The method gives a good maximum power operation of any PV. The advantage of the presented method is that the control system can adapt to different changes that might happen during the life cycle of the PV module. All the analytical and simulation results of this research are presented.

[1]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

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

[3]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[4]  Weidong Xiao,et al.  Topology Study of Photovoltaic Interface for Maximum Power Point Tracking , 2007, IEEE Transactions on Industrial Electronics.

[5]  Weidong Xiao,et al.  Application of Centered Differentiation and Steepest Descent to Maximum Power Point Tracking , 2007, IEEE Transactions on Industrial Electronics.

[6]  Marco Dorigo,et al.  From Natural to Artificial Swarm Intelligence , 1999 .

[7]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[8]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[9]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[10]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[11]  N. Mutoh,et al.  A method for MPPT control while searching for parameters corresponding to weather conditions for PV generation systems , 2004 .

[12]  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.

[13]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[14]  Kostas Kalaitzakis,et al.  Development of a microcontroller-based, photovoltaic maximum power point tracking control system , 2001 .

[15]  M. Masoum,et al.  Theoretical and Experimental Analyses of Photovoltaic Systems with Voltage and Current-Based Maximum Power Point Tracking , 2002, IEEE Power Engineering Review.

[16]  F. Blaabjerg,et al.  Improved MPPT method for rapidly changing environmental conditions , 2006, 2006 IEEE International Symposium on Industrial Electronics.

[17]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[18]  Chih-Chiang Hua,et al.  Study of maximum power tracking techniques and control of DC/DC converters for photovoltaic power system , 1998, PESC 98 Record. 29th Annual IEEE Power Electronics Specialists Conference (Cat. No.98CH36196).

[19]  Chung-Yuen Won,et al.  A new maximum power point tracker of photovoltaic arrays using fuzzy controller , 1994, Proceedings of 1994 Power Electronics Specialist Conference - PESC'94.

[20]  Manolis A. Christodoulou,et al.  A new method for weight updating in fuzzy cognitive maps using system feedback , 2005, ICINCO.

[21]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[22]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[23]  R. W. Dobbins,et al.  Computational intelligence PC tools , 1996 .

[24]  Marcelo Godoy Simões,et al.  Fuzzy optimisation based control of a solar array system , 1999 .

[25]  I. Batarseh,et al.  Maximum Efficiency Point Tracking (MEPT) Method and Digital Dead Time Control Implementation , 2006, IEEE Transactions on Power Electronics.

[26]  P.L. Chapman,et al.  Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques , 2007, IEEE Transactions on Energy Conversion.

[27]  M.A.S. Masoum,et al.  Microprocessor-controlled new class of optimal battery chargers for photovoltaic applications , 2004, IEEE Transactions on Energy Conversion.

[28]  R.A. Dougal,et al.  Power controller design for maximum power tracking in solar installations , 2004, IEEE Transactions on Power Electronics.

[29]  D. B. Snyman,et al.  Combined low-cost, high-efficient inverter, peak power tracker and regulator for PV applications , 1989, 20th Annual IEEE Power Electronics Specialists Conference.