Fractional-order fuzzy controller for a PMSG wind turbine system

This paper presents a novel Takagi–Sugeno (T-S) fractional-order fuzzy logic control for a permanent magnet synchronous generator wind turbine (PMSG-WT) system. The proposed control strategy is composed of two parts. Firstly, a fractional reference model of the PMSG-WT nonlinear system is provided using T-S model and fractional calculus in order to ensure an accurate and robust model of the system. Secondly, the feedback controller gains are determined by resolving a set of linear matrix inequalities. The effectiveness and the validity of the proposed approach are demonstrated using numerical simulations.

[1]  Djalil Boudjehem,et al.  A fractional model for robust fractional order Smith predictor , 2013 .

[2]  Mohamed El Hachemi Benbouzid,et al.  Experimental Validation of a Marine Current Turbine Simulator: Application to a Permanent Magnet Synchronous Generator-Based System Second-Order Sliding Mode Control , 2011, IEEE Transactions on Industrial Electronics.

[3]  Dong-Choon Lee,et al.  Advanced Pitch Angle Control Based on Fuzzy Logic for Variable-Speed Wind Turbine Systems , 2015, IEEE Transactions on Energy Conversion.

[4]  Adam M. Ragheb,et al.  Wind Turbines Theory - The Betz Equation and Optimal Rotor Tip Speed Ratio , 2011 .

[5]  Ramji Tiwari,et al.  Fuzzy Logic Based MPPT for Permanent Magnet Synchronous Generator in wind Energy Conversion System , 2016 .

[6]  Chien-Yu Huang,et al.  Design and Implementation of the Permanent- Magnet Synchronous Generator Drive in Wind Generation Systems , 2018 .

[7]  Tarek Bouktir,et al.  Optimal Reference Model Based Fuzzy Tracking Control for Wind Energy Conversion System , 2016, International Journal of Renewable Energy Research.

[8]  Mohamed Wissem Naouar,et al.  Predictive control strategies for wind turbine system based on permanent magnet synchronous generator. , 2016, ISA transactions.

[9]  Salem Arif,et al.  Optimal design and tuning of novel fractional order PID power system stabilizer using a new metaheuristic Bat algorithm , 2017 .

[10]  Smail Zouggar,et al.  Real Time Study of P&O MPPT Control for Small Wind PMSG Turbine Systems Using Arduino Microcontroller ☆ , 2017 .

[11]  Majid Gandomkar,et al.  Microgrid dynamic responses enhancement using artificial neural network‐genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds , 2016 .

[12]  Il-Yop Chung,et al.  Tuning of the PI Controller Parameters of a PMSG Wind Turbine to Improve Control Performance under Various Wind Speeds , 2015 .

[13]  Amin Hajizadeh,et al.  Robust fractional-order control of PMSG-based WECS , 2015, Int. J. Autom. Control..

[14]  Jihong Wang,et al.  Technical Feasibility Study of Thermal Energy Storage Integration into the Conventional Power Plant Cycle , 2017 .

[15]  Karthikeyan Rajagopal,et al.  Fractional order nonlinear variable speed and current regulation of a permanent magnet synchronous generator wind turbine system , 2016 .

[16]  P. Lino,et al.  New tuning rules for fractional PIα controllers , 2007 .

[17]  A. Boudghene Stambouli,et al.  A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system , 2011, Expert Syst. Appl..

[18]  Karzan Wakil,et al.  Enhanced control strategies for a hybrid battery/photovoltaic system using FGS-PID in grid-connected mode , 2019, International Journal of Hydrogen Energy.

[19]  Alireza Rezvani,et al.  Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode , 2019 .