A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids

Abstract As the solar PV system (SPVS) suffered from an unavoidable complication that it has nonlinearity in I–V curves, the optimum maximum power point (MPP) measurement is difficult under fluctuating climatic conditions. For maximizing SPVS output power, MPP tracking (MPPT) controllers are used. In this paper, a new adaptive fuzzy logic controller (AFLC) based MPPT technique is proposed. In this proposed AFLC, the membership functions (MFs) are optimized using the Grey Wolf Optimization (GWO) technique to generate the optimal duty cycle for MPPT. Four shading patterns are used to experiment with the performance of the proposed AFLC. The proposed approach tracks the global MPP for all shading conditions and also enhances the tracking speed and tracking efficiency with reduced oscillations. The effectiveness and robustness of proposed AFLC based tracker results over P&O and FLC are validated using Matlab/Simulink environment. The proposed AFLC overcome the drawbacks of the classical P&O, and FLC approaches.

[1]  F. Blaabjerg,et al.  Distributed Generation: Toward a New Energy Paradigm , 2010, IEEE Industrial Electronics Magazine.

[2]  Amir Gheibi,et al.  Maximum Power Point Tracking of Photovoltaic Generation Based on the Type 2 Fuzzy Logic Control Method , 2011 .

[3]  H. Mahamudul,et al.  Photovoltaic System Modeling with Fuzzy Logic Based Maximum Power Point Tracking Algorithm , 2013 .

[4]  Necmi Altin,et al.  Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter , 2017 .

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

[6]  Yequn Liu,et al.  Efficient photocatalytic reduction of Cr(VI) in aqueous solution over CoS2/g-C3N4-rGO nanocomposites under visible light , 2020, Applied Surface Science.

[7]  N. V. Srikanth,et al.  Load frequency control for diverse sources of interconnected two area power system: An adaptive fuzzy approach , 2016, 2016 International Conference on Control, Computing, Communication and Materials (ICCCCM).

[8]  Pawan Kumar,et al.  Performance analysis of neural network and fuzzy logic based MPPT techniques for solar PV systems , 2015 .

[9]  Moulay Fatima,et al.  A detailed modeling of photovoltaic module using MATLAB , 2014 .

[10]  S. Geng,et al.  Photocatalytic dehydrogenation of formic acid promoted by a superior PdAg@g-C3N4 Mott–Schottky heterojunction , 2019, Journal of Materials Chemistry A.

[11]  M. Liserre,et al.  Future Energy Systems: Integrating Renewable Energy Sources into the Smart Power Grid Through Industrial Electronics , 2010, IEEE Industrial Electronics Magazine.

[12]  Jan T. Bialasiewicz,et al.  Power-Electronic Systems for the Grid Integration of Renewable Energy Sources: A Survey , 2006, IEEE Transactions on Industrial Electronics.

[13]  Roger A. Dougal,et al.  Parallel-Connected Solar PV System to Address Partial and Rapidly Fluctuating Shadow Conditions , 2009, IEEE Transactions on Industrial Electronics.

[14]  Abdelhamid Rabhi,et al.  Optimization of Scaling Factors of Fuzzy–MPPT Controller for Stand-alone Photovoltaic System by Particle Swarm Optimization , 2017 .

[15]  V. Agarwal,et al.  MATLAB-Based Modeling to Study the Effects of Partial Shading on PV Array Characteristics , 2008, IEEE Transactions on Energy Conversion.

[16]  Muhammad Amjad,et al.  A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm , 2012 .

[17]  Maysam F. Abbod,et al.  A novel maximum power point tracking technique based on fuzzy logic for photovoltaic systems , 2018, International Journal of Hydrogen Energy.

[18]  Saad Mekhilef,et al.  A review on solar energy use in industries , 2011 .

[19]  A. P. Williams,et al.  Impact of anthropogenic climate change on wildfire across western US forests , 2016, Proceedings of the National Academy of Sciences.

[20]  K. Nabti,et al.  Comparison of Perturb & Observe and Fuzzy Logic in Maximum Power Point Tracker for PV Systems , 2014 .

[21]  Nasrudin Abd Rahim,et al.  Assessment of Effect of Haze on Photovoltaic systems in Malaysia due to Open Burning in Sumatra , 2017 .

[22]  Stephen J. Finney,et al.  A Maximum Power Point Tracking Technique for Partially Shaded Photovoltaic Systems in Microgrids , 2013, IEEE Transactions on Industrial Electronics.

[23]  Jaw-Kuen Shiau,et al.  A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables , 2015, Algorithms.

[24]  A. M. Noman,et al.  A fuzzy logic control method for MPPT of PV systems , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[25]  Mansour Souissi,et al.  Modeling and control of photovoltaic and fuel cell based alternative power systems , 2018, International Journal of Hydrogen Energy.

[26]  M. Adly,et al.  An optimized fuzzy maximum power point tracker for stand alone photovoltaic systems: Ant colony approach , 2012, 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[27]  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.

[28]  M. F. Almi,et al.  Advanced Fuzzy MPPT Controller for a Stand-alone PV System☆ , 2014 .

[29]  Yu Zhang,et al.  Comparison of P&O and hill climbing MPPT methods for grid-connected PV converter , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

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

[31]  Stephen J. Finney,et al.  A Modified Stationary Reference Frame-Based Predictive Current Control With Zero Steady-State Error for LCL Coupled Inverter-Based Distributed Generation Systems , 2011, IEEE Transactions on Industrial Electronics.

[32]  Srikanth Nandiraju,et al.  A novel photovoltaic maximum power point tracking technique based on grasshopper optimized fuzzy logic approach , 2020, International Journal of Hydrogen Energy.

[33]  S. Kahla,et al.  Fuzzy-PSO controller design for maximum power point tracking in photovoltaic system , 2017 .

[34]  Weiwei Yang,et al.  Monodisperse PtCu alloy nanoparticles as highly efficient catalysts for the hydrolytic dehydrogenation of ammonia borane , 2018, International Journal of Hydrogen Energy.