Improving Efficiency of Photovoltaic System by Using Neural Network MPPT and Predictive Control of Converter

This paper proposes a new method to extract maximum energy from Photovoltaic (PV) systems. The artificial neural network (ANN) is used to track the maximum power based on the irradiance level and temperature. By using this algorithm the current in which the PV operates at its maximum power is extracted. In addition to ANN, a predictive controller is used to maximize the efficiency of the boost converter. The simulation results verify the suitable performance of the proposed method and this method maximizes the photovoltaic system energy extraction.

[1]  Geoffrey R. Walker,et al.  Evaluating MPPT Converter Topologies Using a Matlab PV Model , 2000 .

[2]  Roohollah Fadaeinedjad,et al.  FPGA-based real time incremental conductance maximum power point tracking controller for photovoltaic systems , 2014 .

[3]  S. B. Kjaer,et al.  Evaluation of the “Hill Climbing” and the “Incremental Conductance” Maximum Power Point Trackers for Photovoltaic Power Systems , 2012, IEEE Transactions on Energy Conversion.

[4]  Ralph Kennel,et al.  Model predictive control -- a simple and powerful method to control power converters , 2009, 2009 IEEE 6th International Power Electronics and Motion Control Conference.

[5]  O. Mohammed,et al.  Smart optimal control of DC-DC boost converter in PV systems , 2010, 2010 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America (T&D-LA).

[6]  M. E. H. Benbouzid,et al.  Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy “ANFIS” , 2013, 2013 Eighth International Conference and Exhibition on Ecological Vehicles and Renewable Energies (EVER).

[7]  Adil Sarwar,et al.  Five parameter modelling and simulation of solar PV cell , 2015, 2015 International Conference on Energy Economics and Environment (ICEEE).

[8]  Anis Sakly,et al.  Comparison between P&O and P.S.O methods based MPPT algorithm for photovoltaic systems , 2015, 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).

[9]  Jorge L. Duarte,et al.  A predictive control scheme for DC voltage and AC current in grid-connected photovoltaic inverters with minimum DC link capacitance , 2001, IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243).

[10]  Simone Buso,et al.  Low-Complexity MPPT Technique Exploiting the PV Module MPP Locus Characterization , 2009, IEEE Transactions on Industrial Electronics.

[11]  Fernando L. M. Antunes,et al.  A maximum power point tracker for PV systems using a high performance boost converter , 2006 .

[12]  Xiao Li,et al.  Maximum Power Point Tracking for Photovoltaic System Using Adaptive Extremum Seeking Control , 2013, IEEE Transactions on Control Systems Technology.

[13]  S Ahmed,et al.  High-Performance Adaptive Perturb and Observe MPPT Technique for Photovoltaic-Based Microgrids , 2011, IEEE Transactions on Power Electronics.

[14]  Ankit Gupta,et al.  Performance analysis of neural network and fuzzy logic based MPPT techniques for solar PV systems , 2014, 2014 6th IEEE Power India International Conference (PIICON).

[15]  Dylan Dah-Chuan Lu,et al.  Steady state reliability of maximum power point tracking algorithms used with a thermoelectric generator , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[16]  N. H. Helwa,et al.  Maximum power point traking controller for PV systems using neural networks , 2005 .

[17]  Saad Mekhilef,et al.  Simulation and Hardware Implementation of Incremental Conductance MPPT With Direct Control Method Using Cuk Converter , 2011, IEEE Transactions on Industrial Electronics.

[18]  Honghua Wang,et al.  Maximum power point tracking of photovoltaic generation based on the fuzzy control method , 2009, 2009 International Conference on Sustainable Power Generation and Supply.