An Advanced Maximum Power Point Tracking Method for Photovoltaic Systems by Using Variable Universe Fuzzy Logic Control Considering Temperature Variability

In this study maximum power point tracking (MPPT) is applied to the photovoltaic (PV) system to harvest the maximum power output. The output power of the PV effect changes according to external solar irradiation and ambient temperature conditions. In the existing MPPT strategies, most of them only take variations in radiation level into account, rarely considering the impact of temperature changes. However, the temperature coefficients (TC) play an important role in the PV system, especially in applications where ambient temperature changes are relatively large. In this paper, an MPPT method is presented for a PV system that considers the temperature change by using variable universe fuzzy logic control (VUFLC). By considering the ambient temperature change in PV modules, the proposed control method can regulate the contraction and expansion factor of VUFLC, which eliminates the influence of temperature variability and improves the performance of MPPT, therefore achieving fast and accurate tracking control. The proposed method was evaluated for a PV module under different ambient conditions and its control performance is compared with other MPPT strategies by simulation and experimental results.

[1]  Pr Djamila Rekioua.  Optimization of Photovoltaic Power Systems , 2012 .

[2]  Soedibyo,et al.  The comparative study of Buck-boost, Cuk, Sepic and Zeta converters for maximum power point tracking photovoltaic using P&O method , 2015, 2015 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE).

[3]  Soteris A. Kalogirou,et al.  MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives , 2014 .

[4]  Issa Batarseh,et al.  Improved solar PV cell Matlab simulation model and comparison , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

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

[6]  Tariq Iqbal,et al.  Comparing bisection numerical algorithm with fractional short circuit current and open circuit voltage methods for MPPT photovoltaic systems , 2016, 2016 IEEE Electrical Power and Energy Conference (EPEC).

[7]  I. Ngamroo,et al.  Optimal Superconducting Coil Integrated Into PV Generators for Smoothing Power and Regulating Voltage in Distribution System With PHEVs , 2016, IEEE Transactions on Applied Superconductivity.

[8]  Ali Hilal Mutlag,et al.  Design optimization of solar power system with respect to temperature and sun tracking , 2016, 2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA).

[9]  Whei-Min Lin,et al.  Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System , 2011, IEEE Transactions on Power Electronics.

[10]  N. Rajasekar,et al.  A comprehensive review on solar PV maximum power point tracking techniques , 2017 .

[11]  Divya Modani,et al.  Improvement in efficiency of PV module using soft computing based MPPT , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[12]  Ali M. Eltamaly,et al.  A comprehensive comparison of different MPPT techniques for photovoltaic systems , 2015 .

[13]  Dan-yang Cao,et al.  Variable universe fuzzy expert system for aluminum electrolysis , 2011 .

[14]  Jianjun Hu,et al.  Fuzzy Logic Controllers for Specialty Vehicles Using a Combination of Phase Plane Analysis and Variable Universe Approach , 2017, IEEE Access.

[15]  Weiwei Shan,et al.  Analog Circuit Implementation of a Variable Universe Adaptive Fuzzy Logic Controller , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[16]  Bidyadhar Subudhi,et al.  A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power Systems , 2013, IEEE Transactions on Sustainable Energy.

[17]  Yize Sun,et al.  An Enhanced MPPT Method Combining Fractional-Order and Fuzzy Logic Control , 2017, IEEE Journal of Photovoltaics.

[18]  Minwon Park,et al.  A study on the optimal voltage for MPPT obtained by surface temperature of solar cell , 2004, 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004.

[19]  Jing Chen,et al.  Variable Universe Adaptive Fuzzy Control for Liquid Lever , 2008, 2008 International Symposium on Computational Intelligence and Design.

[20]  N. Jenkins,et al.  A model of PV generation suitable for stability analysis , 2004, IEEE Transactions on Energy Conversion.

[21]  Tao Peng,et al.  A MATLAB-Simulink-Based PV Module Model and Its Application Under Conditions of Nonuniform Irradiance , 2012, IEEE Transactions on Energy Conversion.

[22]  Mohammad Hassan Moradi,et al.  Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review , 2013 .

[23]  Roberto F. Coelho,et al.  An Optimized Maximum Power Point Tracking Method Based on PV Surface Temperature Measurement , 2012 .

[24]  Mohamed M. Algazar,et al.  Maximum Power Point Tracking Using Fuzzy Logic Control , 2011 .

[25]  Marcelo Gradella Villalva,et al.  Modeling and circuit-based simulation of photovoltaic arrays , 2009, 2009 Brazilian Power Electronics Conference.

[26]  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 .

[27]  Tomonobu Senjyu,et al.  Fuzzy Control of Distributed PV Inverters/Energy Storage Systems/Electric Vehicles for Frequency Regulation in a Large Power System , 2013, IEEE Transactions on Smart Grid.

[28]  Robert S. Balog,et al.  MPPT of Photovoltaic Systems Using Sensorless Current-Based Model Predictive Control , 2017, IEEE Transactions on Industry Applications.

[29]  Kenneth Tze Kin Teo,et al.  Fuzzy Logic Based MPPT for Photovoltaic Modules Influenced by Solar Irradiation and Cell Temperature , 2011, 2011 UkSim 13th International Conference on Computer Modelling and Simulation.

[30]  Denizar Cruz Martins,et al.  MPPT method based on temperature control of the photovoltaic cells , 2016, 2016 12th IEEE International Conference on Industry Applications (INDUSCON).

[31]  Mohammad Hassan Moradi,et al.  A Function-Based Maximum Power Point Tracking Method for Photovoltaic Systems , 2016, IEEE Transactions on Power Electronics.

[32]  Carlos Robles Algarín,et al.  A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller , 2018 .

[33]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for photovoltaic applications: A review , 2008 .

[34]  Xiao Li,et al.  Detection of Internal Resistance Change for Photovoltaic Arrays Using Extremum-Seeking Control MPPT Signals , 2016, IEEE Transactions on Control Systems Technology.

[35]  Ashwin Kumar Sahoo,et al.  Fuzzy logic controller based maximum power point tracking for PV system , 2016, 2016 3rd International Conference on Electrical Energy Systems (ICEES).

[36]  Hongxing Li,et al.  Variable universe stable adaptive fuzzy control of nonlinear system , 2002 .

[37]  Amruta Deshpande,et al.  Modeling and simulation of Incremental conductance Maximum Power Point tracking , 2015, 2015 International Conference on Energy Systems and Applications.

[38]  Engin Karatepe,et al.  Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions , 2009 .