A novel maximum power point tracking technique based on fuzzy logic for photovoltaic systems

Abstract Maximum power point tracking (MPPT) techniques are considered a crucial part in photovoltaic system design to maximise the output power of a photovoltaic array. Whilst several techniques have been designed, Perturb and Observe (P&O) is widely used for MPPT due to its low cost and simple implementation. Fuzzy logic (FL) is another common technique that achieves vastly improved performance for MPPT technique in terms of response speed and low fluctuation about the maximum power point. However, major issues of the conventional FL-MPPT are a drift problem associated with changing irradiance and complex implementation when compared with the P&O-MPPT. In this paper, a novel MPPT technique based on FL control and P&O algorithm is presented. The proposed method incorporates the advantages of the P&O-MPPT to account for slow and fast changes in solar irradiance and the reduced processing time for the FL-MPPT to address complex engineering problems when the membership functions are few. To evaluate the performance, the P&O-MPPT, FL-MPPT and the proposed method are simulated by a MATLAB-SIMULINK model for a grid-connected PV system. The EN 50530 standard test is used to calculate the efficiency of the proposed method under varying weather conditions. The simulation results demonstrate that the proposed technique accurately tracks the maximum power point and avoids the drift problem, whilst achieving efficiencies of greater than 99.6%.

[1]  M. Vitelli,et al.  Optimization of perturb and observe maximum power point tracking method , 2005, IEEE Transactions on Power Electronics.

[2]  S. S. Mortazavi,et al.  A new MPPT scheme based on a novel fuzzy approach , 2017 .

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

[4]  Essam E. M. Mohamed,et al.  Modified efficient perturb and observe maximum power point tracking technique for grid-tied PV system , 2018, International Journal of Electrical Power & Energy Systems.

[5]  Chokri Ben Salah,et al.  Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems , 2011 .

[6]  Martin Ordonez,et al.  Zero Oscillation and Irradiance Slope Tracking for Photovoltaic MPPT , 2014, IEEE Transactions on Industrial Electronics.

[7]  B N Alajmi,et al.  Fuzzy-Logic-Control Approach of a Modified Hill-Climbing Method for Maximum Power Point in Microgrid Standalone Photovoltaic System , 2011, IEEE Transactions on Power Electronics.

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

[9]  Ekaitz Zulueta,et al.  Novel control algorithm for MPPT with Boost converters in photovoltaic systems , 2017 .

[10]  Adel El-Shahat,et al.  A Novel MPPT Algorithm Based on Particle Swarm Optimization for Photovoltaic Systems , 2017, IEEE Transactions on Sustainable Energy.

[11]  Hassan Fathabadi,et al.  Novel fast dynamic MPPT (maximum power point tracking) technique with the capability of very high accurate power tracking , 2016 .

[12]  Ali Zilouchian,et al.  A proposed maximum power point tracking algorithm based on a new testing standard , 2013 .

[13]  Boutaib Dahhou,et al.  Adaptive fuzzy controller based MPPT for photovoltaic systems , 2014 .

[14]  C. Larbes,et al.  Comparative study and performance evaluation of central and distributed topologies of photovoltaic system , 2017 .

[15]  Mohammed A. Hannan,et al.  Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller , 2012 .

[16]  Luigi Piegari,et al.  Adaptive perturb and observe algorithm for photovoltaic maximum power point tracking , 2010 .

[17]  M. Muthuramalingam,et al.  Comparative analysis of distributed MPPT controllers for partially shaded stand alone photovoltaic systems , 2014 .

[18]  Necmi Altin,et al.  Three-phase three-level grid interactive inverter with fuzzy logic based maximum power point tracking controller , 2013 .

[19]  Samir Moulahoum,et al.  M5P model tree based fast fuzzy maximum power point tracker , 2018 .

[20]  Sami Kahla,et al.  Energy storage based on maximum power point tracking in photovoltaic systems: A comparison between GAs and PSO approaches , 2015 .

[21]  Cosku Kasnakoglu,et al.  Performance improvement of a photovoltaic system using a controller redesign based on numerical modeling , 2016 .

[22]  Z. Salam,et al.  A Modified P&O Maximum Power Point Tracking Method With Reduced Steady-State Oscillation and Improved Tracking Efficiency , 2016, IEEE Transactions on Sustainable Energy.

[23]  B. Zahawi,et al.  Assessment of the Incremental Conductance Maximum Power Point Tracking Algorithm , 2013, IEEE Transactions on Sustainable Energy.

[24]  Y.S. Boutalis,et al.  New maximum power point tracker for PV arrays using fuzzy controller in close cooperation with fuzzy cognitive networks , 2006, IEEE Transactions on Energy Conversion.

[25]  Nikita Gupta,et al.  Tuning of asymmetrical fuzzy logic control algorithm for SPV system connected to grid , 2017 .

[26]  Saad Mekhilef,et al.  State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review , 2016 .

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

[28]  S. K. Kollimalla,et al.  A Novel Adaptive P&O MPPT Algorithm Considering Sudden Changes in the Irradiance , 2014, IEEE Transactions on Energy Conversion.

[29]  Mohd Amran Mohd Radzi,et al.  Development of adaptive perturb and observe-fuzzy control maximum power point tracking for photovoltaic boost dc-dc converter , 2014 .

[30]  Roland Brundlinger,et al.  prEN 50530 - The New European Standard for Performance Characterisation of PV Inverters , 2009 .

[31]  Sabrina Abdeddaim,et al.  Optimal energy control of a PV-fuel cell hybrid system , 2017 .

[32]  Hassan Fathabadi,et al.  Novel high efficiency DC/DC boost converter for using in photovoltaic systems , 2016 .

[33]  Tahar Bahi,et al.  Reactive power compensation control for three phase grid-connected photovoltaic generator , 2015 .

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

[35]  Xiaofeng Wu,et al.  Maximum power point tracking using a variable antecedent fuzzy logic controller , 2016 .

[36]  Luiz A. C. Lopes,et al.  Comparative study of variable size perturbation and observation maximum power point trackers for PV systems , 2010 .

[37]  Jubaer Ahmed,et al.  The application of soft computing methods for MPPT of PV system: A technological and status review , 2013 .

[38]  Pedro Ibañez,et al.  Intelligent PV Module for Grid-Connected PV Systems , 2006, IEEE Transactions on Industrial Electronics.

[39]  Suttichai Premrudeepreechacharn,et al.  Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system , 2005 .

[40]  Susovon Samanta,et al.  Modified Perturb and Observe MPPT Algorithm for Drift Avoidance in Photovoltaic Systems , 2015, IEEE Transactions on Industrial Electronics.

[41]  Bhim Singh,et al.  Complementary performance enhancement of PV energy system through thermoelectric generation , 2016 .

[42]  V. Kamala Devi,et al.  A modified Perturb & Observe MPPT technique to tackle steady state and rapidly varying atmospheric conditions , 2017 .

[43]  Ismail H. Altas,et al.  A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems , 2016, Neural Computing and Applications.

[44]  Mohammed Ouassaid,et al.  A new variable step size INC MPPT method for PV systems , 2014, 2014 International Conference on Multimedia Computing and Systems (ICMCS).

[45]  Jawad Ahmad,et al.  A fractional open circuit voltage based maximum power point tracker for photovoltaic arrays , 2010, 2010 2nd International Conference on Software Technology and Engineering.