Maximum power point tracking in photovoltaic systems using indirect adaptive fuzzy robust controller

A photovoltaic system is used to produce energy that is dependent on environmental conditions such as irradiance, temperature, and the load attached to it. Due to the nonlinear characteristics, parameter uncertainties and load disturbance of photovoltaic (PV) systems and the problem of low efficiency due to the variation of environmental conditions, the maximum power point tracking method is required to extract the maximum power from the PV system. So, in this paper a novel indirect adaptive fuzzy fractional-order sliding mode is proposed to tackle this challenge. This method is based on a two-loop method. The first loop is designed to seek the maximum power point, and the second loop is designed to track the maximum power point. At first, a novel fractional-order sliding surface is proposed. Then, chattering in control signal is removed using a continuous function. In this paper, an indirect adaptive fuzzy system is used to estimate the unknown function of the system and adaptation laws are obtained based on the Lyapunov theory. The performance of the photovoltaic system is investigated by applying the proposed method, conventional sliding mode controller, integral sliding mode controller and backstepping sliding mode controller. Simulation results indicate the effectiveness of the proposed method.

[1]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[2]  Saleem Abdullah,et al.  Triangular cubic linguistic hesitant fuzzy aggregation operators and their application in group decision making , 2018, J. Intell. Fuzzy Syst..

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

[4]  Saleem Abdullah,et al.  Trapezoidal cubic fuzzy number Einstein hybrid weighted averaging operators and its application to decision making , 2019, Soft Comput..

[5]  M. Salhi,et al.  Maximum Power Point Tracking Controller for PV Systems using a PI Regulator with Boost DC/DC Converter , 2009 .

[6]  Fernando Tadeo,et al.  Backstepping sliding mode control for maximum power point tracking of a photovoltaic system , 2017 .

[7]  Bindeshwar Singh,et al.  A review on distributed generation planning , 2017 .

[8]  Saleem Abdullah,et al.  Precursor Selection for Sol–Gel Synthesis of Titanium Carbide Nanopowders by a New Cubic Fuzzy Multi-Attribute Group Decision-Making Model , 2019, J. Intell. Syst..

[9]  Mohammad Ali Badamchizadeh,et al.  Second-order fuzzy sliding-mode control of photovoltaic power generation systems , 2017 .

[10]  Samaher AlJanabi,et al.  Multi Objectives Optimization to Gas Flaring Reduction from Oil Production , 2019, Big Data and Networks Technologies.

[11]  Samaher Al-Janabi,et al.  A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation , 2019, Soft Computing.

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

[13]  Chian-Song Chiu,et al.  Terminal sliding mode control for maximum power point tracking of photovoltaic power generation systems , 2012 .

[14]  Yogesh K. Chauhan,et al.  A comparative investigation of maximum power point tracking methods for solar PV system , 2016 .

[15]  Samaher Al-Janabi,et al.  An Innovative synthesis of deep learning techniques (DCapsNet & DCOM) for generation electrical renewable energy from wind energy , 2020, Soft Computing.

[16]  Saleem Abdullah,et al.  Cubic uncertain linguistic powered Einstein aggregation operators and their application to multi-attribute group decision making , 2019, Mathematical Sciences.

[17]  Saleem Abdullah,et al.  Some geometric operators with triangular cubic linguistic hesitant fuzzy number and their application in group decision-making , 2018, J. Intell. Fuzzy Syst..

[18]  G. Petrone,et al.  A two-steps algorithm improving the P&O steady state MPPT efficiency , 2014 .

[19]  Juel Rana,et al.  Modelling of PV module with incremental conductance MPPT controlled buck-boost converter , 2013, 2013 2nd International Conference on Advances in Electrical Engineering (ICAEE).

[20]  Saleem Abdullah,et al.  Trapezoidal Linguistic Cubic Fuzzy TOPSIS Method and Application in a Group Decision Making Program , 2020, J. Intell. Syst..

[21]  Samaher Al-Janabi,et al.  Evaluation prediction techniques to achievement an optimal biomedical analysis , 2019, Int. J. Grid Util. Comput..

[22]  Sudha Arora,et al.  Maximum power point tracking methodologies for solar PV systems – A review , 2017 .

[23]  Tarek Selmi,et al.  P&O MPPT implementation using MATLAB/Simulink , 2014, 2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER).

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

[25]  Mohammad Rasool Mojallizadeh,et al.  Designing a new robust sliding mode controller for maximum power point tracking of photovoltaic cells , 2016 .

[26]  Florentin Smarandache,et al.  Triangular Cubic Hesitant Fuzzy Einstein Hybrid Weighted Averaging Operator and Its Application to Decision Making , 2018, Symmetry.

[27]  Oscar Barambones,et al.  MPPT controller for a photovoltaic power system based on increment conductance approach , 2013, 2013 International Conference on Renewable Energy Research and Applications (ICRERA).

[28]  Mehran Sabahi,et al.  Chattering free full-order terminal sliding-mode control for maximum power point tracking of photovoltaic cells , 2017 .

[29]  Saleem Abdullah,et al.  Trapezoidal cubic hesitant fuzzy aggregation operators and their application in group decision-making , 2019, J. Intell. Fuzzy Syst..

[30]  Malihe M. Farsangi,et al.  MAXIMUM POWER POINT TRACKING USING SLIDING MODE CONTROL FOR PHOTOVOLTAIC ARRAY , 2013 .

[31]  Saleem Abdullah,et al.  Aggregation operators on triangular cubic fuzzy numbers and its application to multi-criteria decision making problems , 2017, J. Intell. Fuzzy Syst..

[32]  Bidyadhar Subudhi,et al.  Double Integral Sliding Mode MPPT Control of a Photovoltaic System , 2016, IEEE Transactions on Control Systems Technology.

[33]  Florentin Smarandache,et al.  Group Decision Making Based on Triangular Neutrosophic Cubic Fuzzy Einstein Hybrid Weighted Averaging Operators , 2019, Symmetry.

[34]  Ahmad B. Rad,et al.  Indirect adaptive fuzzy sliding mode control: Part I: fuzzy switching , 2001, Fuzzy Sets Syst..

[35]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[36]  S. H. Ali,et al.  Miner for OACCR: Case of medical data analysis in knowledge discovery , 2012, 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT).

[37]  Samaher Al-Janabi,et al.  Evaluation prediction techniques to achievement an optimal biomedical analysis , 2019 .

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

[39]  Zaidi Asma,et al.  Maximum Power Point Tracking of Photovoltaic Systems Based on Fast Terminal Sliding Mode Controller , 2016, International Journal of Renewable Energy Research.

[40]  G. Dileep,et al.  Selection of non-isolated DC-DC converters for solar photovoltaic system , 2017 .

[41]  Aziz Naamane,et al.  Second order sliding mode-based MPPT control for photovoltaic applications , 2017 .

[42]  Saleem Abdullah,et al.  Cubic fuzzy Einstein aggregation operators and its application to decision-making , 2018, Int. J. Syst. Sci..

[43]  Samaher Al-Janabi,et al.  A new method for prediction of air pollution based on intelligent computation , 2020, Soft Comput..

[44]  Saleem Abdullah,et al.  Dealer using a new trapezoidal cubic hesitant fuzzy TOPSIS method and application to group decision-making program , 2019, Soft Comput..