A machine learning approach for active/reactive power control of grid-connected doubly-fed induction generators

Abstract This paper suggests a new fuzzy method for active power (AP) and reactive power (RP) control of a power grid that includes wind turbines and Doubly Fed Induction Generators (DFIGs). A Recurrent Type-II Fuzzy Neural Networks (RT2FNN) controller based on Radial Basis Function Networks (RBFN) is applied to the rotor side converter for the power control and voltage regulation of the wind turbine equipped with the DFIG. In order to train a model, the voltage profile at each bus, and the reactive power of the power grid are given to the RT2FNN as the input and output, respectively. A wind turbine and its control units are studied in detail. Simulation results, obtained in MATLAB software, show the well performance, robustness, good accuracy and power quality improvement of the suggested controller in the wind-driven DFIGs.

[1]  Mohammad Reza Khalghani,et al.  Resilient Frequency Control Design for Microgrids Under False Data Injection , 2021, IEEE Transactions on Industrial Electronics.

[2]  Lie Xu,et al.  Direct active and reactive power control of DFIG for wind energy generation , 2006, IEEE Transactions on Energy Conversion.

[3]  K. V. Shihabudheen,et al.  Neuro-Fuzzy Control of DFIG Wind Energy System with Distribution Network , 2018, Electric Power Components and Systems.

[4]  Venkata Ratnam Kolluru,et al.  Variable nonlinear gain fuzzy with improved synchronous reference frame control strategy for performance enhancement of unified power quality conditioner , 2020 .

[5]  Young Hoon Joo,et al.  Fuzzy Sampled-Data Control for DFIG-Based Wind Turbine With Stochastic Actuator Failures , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[6]  Jafar Tavoosi.  Sliding mode control of a class of nonlinear systems based on recurrent type-2 fuzzy RBFN , 2020, Int. J. Mechatronics Autom..

[7]  Mohammad Bagher Menhaj,et al.  Stability Analysis of a Class of MIMO Recurrent Type-2 Fuzzy Systems , 2017, Int. J. Fuzzy Syst..

[8]  G. N. Pillai,et al.  Recent advances in neuro-fuzzy system: A survey , 2018, Knowl. Based Syst..

[9]  A. El-Shafie,et al.  Investigating the reliability of machine learning algorithms as a sustainable tool for total suspended solid prediction , 2021 .

[10]  G. N. Pillai,et al.  Design and Implementation of Type-2 Fuzzy Logic Controller for DFIG-Based Wind Energy Systems in Distribution Networks , 2016, IEEE Transactions on Sustainable Energy.

[11]  Jafar Tavoosi,et al.  A New Type-II Fuzzy System for Flexible-Joint Robot Arm Control , 2019, 2019 6th International Conference on Control, Instrumentation and Automation (ICCIA).

[12]  Salah Kamel,et al.  Sensitive reactive power dispatch solution accomplished with renewable energy allocation using an enhanced coyote optimization algorithm , 2020 .

[13]  Ibrahim Ziedan,et al.  Machine Learning Techniques for Satellite Fault Diagnosis , 2020 .

[14]  K. V. Shihabudheen,et al.  Control for grid-connected DFIG-based wind energy system using adaptive neuro-fuzzy technique , 2018 .

[15]  Chandra Prakash Gupta,et al.  Design and implementation of interval type-2 fuzzy logic-PI based adaptive controller for DFIG based wind energy system , 2020 .

[16]  Mohamed Abid,et al.  Design and implementation of reconfigurable MPPT fuzzy controller for photovoltaic systems , 2020, Ain Shams Engineering Journal.

[17]  K. Kamal,et al.  Corrosion detection and severity level prediction using acoustic emission and machine learning based approach , 2021 .

[18]  Mojtaba Ahmadieh Khanesar,et al.  Support Vector Regression for Multi-objective Parameter Estimation of Interval Type-2 Fuzzy Systems , 2019, SocProS.

[19]  J. Deuse,et al.  Fuzzy Logic Supervisor-Based Primary Frequency Control Experiments of a Variable-Speed Wind Generator , 2009, IEEE Transactions on Power Systems.

[20]  Mohammad Teshnehlab,et al.  Nonlinear System Identification Using Type-2 Fuzzy Recurrent Wavelet Neural Network , 2019, 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS).

[21]  Mehrdad Saif,et al.  A New Topology of a Fast Proactive Hybrid DC Circuit Breaker for MT-HVDC Grids , 2019 .

[22]  Erkan Kayacan,et al.  A novel fractional-order type-2 fuzzy control method for online frequency regulation in ac microgrid , 2020, Eng. Appl. Artif. Intell..

[23]  Mohammad Shahidehpour,et al.  Fuzzy-Logic Based Frequency Controller for Wind Farms Augmented With Energy Storage Systems , 2016, IEEE Transactions on Power Systems.

[24]  Amr E. Mohamed,et al.  Solving the motion planning problem using learning experience through case-based reasoning and machine learning algorithms , 2020 .

[25]  Patricia Melin,et al.  Extension of the Fuzzy Sugeno Integral Based on Generalized Type-2 Fuzzy Logic , 2020 .

[26]  Mehrdad Saif,et al.  A Bidirectional Power Charging Control Strategy for Plug-in Hybrid Electric Vehicles , 2019, Sustainability.

[27]  R. K. Patnaik,et al.  A new islanding detection and classification technique for the grid connected DFIG wind farms , 2015, 2015 IEEE Power, Communication and Information Technology Conference (PCITC).

[28]  Ahmad M. El-Nagar,et al.  Direct adaptive interval type-2 fuzzy logic controller for the multivariable anaesthesia system , 2011 .

[29]  Mohammad Hassan Khooban,et al.  A Novel Deep Learning Backstepping Controller-Based Digital Twins Technology for Pitch Angle Control of Variable Speed Wind Turbine , 2020 .

[30]  N. C. Kar,et al.  Design and Implementation of Neuro-Fuzzy Vector Control for Wind-Driven Doubly-Fed Induction Generator , 2011, IEEE Transactions on Sustainable Energy.

[31]  R. El-Sehiemy,et al.  An interval branch and bound global optimization algorithm for parameter estimation of three photovoltaic models , 2020 .

[32]  N Bounar,et al.  PSO-GSA based fuzzy sliding mode controller for DFIG-based wind turbine. , 2019, ISA transactions.

[33]  Abdellah Mansouri,et al.  Performance enhancements of DFIG wind turbine using fuzzy-feedback linearization controller augmented by high-gain observer , 2020 .

[34]  Saad Mekhilef,et al.  New grid synchronization and power control scheme of doubly-fed induction generator based wind turbine system using fuzzy logic control , 2020, Comput. Electr. Eng..

[35]  Ivan R. S. Casella,et al.  An ANFIS Power Control for Wind Energy Generation in Smart Grid Scenario Using Wireless Coded OFDM-16-QAM , 2014 .

[36]  Sushmita Mitra,et al.  Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..

[37]  Mehrdad Saif,et al.  An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems , 2019 .

[38]  Habib Benbouhenni,et al.  A comparison study between fuzzy PWM and SVM inverter in NSMC control of stator active and reactive power control of a DFIG based wind turbine systems , 2019, International Journal of Applied Power Engineering (IJAPE).

[39]  Ardashir Mohammadzadeh,et al.  Energy management in photovoltaic battery hybrid systems: A novel type-2 fuzzy control , 2020 .

[40]  J.A.L. Barreiros,et al.  Improving power system dynamic behavior through doubly fed induction machines controlled by static converter using fuzzy control , 2004, IEEE Transactions on Power Systems.

[41]  Ahmad M. El-Nagar,et al.  Practical Implementation for the interval type-2 fuzzy PID controller using a low cost microcontroller , 2014 .

[42]  Patricia Melin,et al.  Introduction to the Type-2 Fuzzy Sugeno Integral , 2019, Extension of the Fuzzy Sugeno Integral Based on Generalized Type-2 Fuzzy Logic.

[43]  Ahmed G. Abo-Khalil,et al.  Synchronization of DFIG output voltage to utility grid in wind power system , 2012 .

[44]  Jafar Tavoosi,et al.  An experimental study on inverse adaptive neural fuzzy control for nonlinear systems , 2020, Int. J. Knowl. Based Intell. Eng. Syst..

[45]  Lie Xu,et al.  Improved Direct Power Control of Doubly-Fed Induction Generator Based Wind Energy System , 2007, 2007 IEEE International Electric Machines & Drives Conference.