Implementation of photovoltaic array MPPT through fixed step predictive control technique

This paper proposes the implementation of Photovoltaic (PV) array Maximum Power Point Tracker (MPPT) through Fixed Step-Model Predictive Controller (FS MPC). The proposed controller scheme is based on the modified INcremental Conductance (INC) algorithm combined with the two-step horizon FS MPC. The current based INC algorithm is subject to major modifications in order to be capable of real time interaction between the MPPT and the controller obtaining sufficient information in one sampling time. The developed technique has been incorporated into a model for the overall simulation of the performance of a PV array for solar energy exploitation and is compared to the conventional approach under solar radiation variation improving PV system utilization efficiency and enabling to optimize system performance. This study also illustrates the effectiveness of the proposed controller scheme under various sky conditions with a simulation model employing real solar radiation data.

[1]  E. Guillot,et al.  An adaptive temperature control law for a solar furnace , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[2]  N. Ammasai Gounden,et al.  Fuzzy logic controller with MPPT using line-commutated inverter for three-phase grid-connected photovoltaic systems , 2009 .

[3]  Chian-Song Chiu.  T-S Fuzzy Maximum Power Point Tracking Control of Solar Power Generation Systems , 2010, IEEE Transactions on Energy Conversion.

[4]  Marcelo Gradella Villalva,et al.  Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays , 2009, IEEE Transactions on Power Electronics.

[5]  Tun-Ping Teng,et al.  Research and development of maximum power transfer tracking system for solar cell unit by matching impedance , 2010 .

[6]  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 .

[7]  S. Gonzalez,et al.  Development of a MATLAB/Simulink Model of a Single-Phase Grid-Connected Photovoltaic System , 2009, IEEE Transactions on Energy Conversion.

[8]  S. Armstrong,et al.  A new methodology to optimise solar energy extraction under cloudy conditions , 2010 .

[9]  U. Ammann,et al.  Model Predictive Control—A Simple and Powerful Method to Control Power Converters , 2009, IEEE Transactions on Industrial Electronics.

[10]  M. Alonso-Abella,et al.  Analysis of the maximum power point tracking in the photovoltaic grid inverters of 5kW , 2009 .

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

[12]  N. Dasgupta,et al.  High-Performance Algorithms for Drift Avoidance and Fast Tracking in Solar MPPT System , 2008, IEEE Transactions on Energy Conversion.

[13]  Yen-Shin Lai,et al.  A Biological Swarm Chasing Algorithm for Tracking the PV Maximum Power Point , 2010, IEEE Transactions on Energy Conversion.

[14]  Chih-Chiang Hua,et al.  A Digital Predictive Current Control With Improved Sampled Inductor Current for Cascaded Inverters , 2009, IEEE Transactions on Industrial Electronics.

[15]  Andrey V. Savkin,et al.  A model predictive control approach to the problem of wind power smoothing with controlled battery storage , 2010 .

[16]  Marian P. Kazmierkowski,et al.  “Predictive control in power electronics and drives” , 2008, 2008 IEEE International Symposium on Industrial Electronics.

[17]  Ching-Tsai Pan,et al.  A Novel Sensorless MPPT Controller for a High-Efficiency Microscale Wind Power Generation System , 2010, IEEE Transactions on Energy Conversion.

[18]  Fabrice Locment,et al.  Maximum power tracking for photovoltaic power system: Development and experimental comparison of two algorithms , 2010 .

[19]  Engin Karatepe,et al.  Polar coordinated fuzzy controller based real-time maximum-power point control of photovoltaic system , 2009 .