Maximum power point tracking system for stand alone solar PV power system using Adaptive Neuro-Fuzzy Inference System

A Maximum Power Point Tracking controller for stand alone solar PV power system is presented in this paper. A solar PV power system consists of PV Module, DC-DC boost converter and an Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller is developed in MATLAB/Simulink. The operation of the DC-DC Boost converter in the PV system is controlled by ANFIS MPPT controller. The ANFIS model is trained using randomly selected data obatined for various temperature and irradiation levels. The cell temperature and irradiation level are the input to the controller and the output of the controller is duty cycle. The model is simulated for various weather conditions and the simulation results are obtained. Performance of proposed ANFIS MPPT controller is analysed by comparing its results with the PV systems without MPPT and PV systems with Incremental Conductance MPPT.

[1]  D. Devaraj,et al.  Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions , 2013 .

[2]  A.M. Al-Ibrahim,et al.  Soft-Computing Model-Based Controllers for Increased Photovoltaic Plant Efficiencies , 2007, IEEE Transactions on Energy Conversion.

[3]  D. Devaraj,et al.  Simulation of Incremental Conductance MPPT based two phase interleaved boost converter using MATLAB/Simulink , 2015, 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT).

[4]  Mohammad A. S. Masoum,et al.  Closure on "Theoretical and experimental analyses of photovoltaic systems with voltage and current-based maximum power point tracking" , 2002 .

[5]  D. Devaraj,et al.  Simulation and analysis of stand-alone photovoltaic system with boost converter using MATLAB/Simulink , 2014, 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014].

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

[8]  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.

[9]  S. Sheik Mohammed.  Modeling and Simulation of Photovoltaic module using MATLAB/Simulink , 2011 .

[10]  Rashad M. Kamel,et al.  A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system , 2010 .

[11]  P. Sirisuk,et al.  A novel ANFIS controller for maximum power point tracking in photovoltaic systems , 2003, The Fifth International Conference on Power Electronics and Drive Systems, 2003. PEDS 2003..

[12]  Andres Barrado,et al.  Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems , 2006 .

[13]  Haitham Abu-Rub,et al.  Adaptive neuro-fuzzy inference system-based maximum power point tracking of solar PV modules for fast varying solar radiations , 2012 .