Fast Finite Time Robust-Adaptive Terminal Sliding Mode Approaches for DFIG wind Turbines in the Presence of Unbounded Perturbations
محورهای موضوعی : Electrical EngineeringSeyed Mahyar Mehdizadeh Moghadam 1 , Esmail Alibeiki 2 , alireza khosravi 3
1 - Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran.
2 - Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran.
3 - Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.
کلید واژه: DFIG, Wind turbine, sliding mode, Fast terminal sliding mode, Adaptive control, Parameter uncertainty, External disturbance,
چکیده مقاله :
This paper proposes a new approach of Fast Finite Time Terminal Adaptive Sliding Mode (FFTASM) control for perturbed Dual-Feed Induction Generators (DFIG)-based wind turbines which are exposed to unbounded modeling uncertainties and external disturbances. Two separate FFTASM controllers are designed for both turbine and generator sections. The proposed approach can control the maximum power at low wind speeds in Zone II of the Wind Energy Conversion System (WECS), as well as the generated power in the presence of unknown unbounded perturbation in Zone III. Considering the dependence of the modeling uncertainties and external disturbances on respectively the dynamic parameters of the wind turbine and its status, it is assumed that the upper bounds of the sum of the uncertainties and disturbances are unknown nonlinear functions of the wind turbine state variables. These unknown functions are estimated using stable adaptive rules. The fast finite-time stability is proved using Lyapunov's theorem in both turbine and generator sections. Numerical simulations at the end of the paper confirm the correctness of the designed control approached
This paper proposes a new approach of Fast Finite Time Terminal Adaptive Sliding Mode (FFTASM) control for perturbed Dual-Feed Induction Generators (DFIG)-based wind turbines which are exposed to unbounded modeling uncertainties and external disturbances. Two separate FFTASM controllers are designed for both turbine and generator sections. The proposed approach can control the maximum power at low wind speeds in Zone II of the Wind Energy Conversion System (WECS), as well as the generated power in the presence of unknown unbounded perturbation in Zone III. Considering the dependence of the modeling uncertainties and external disturbances on respectively the dynamic parameters of the wind turbine and its status, it is assumed that the upper bounds of the sum of the uncertainties and disturbances are unknown nonlinear functions of the wind turbine state variables. These unknown functions are estimated using stable adaptive rules. The fast finite-time stability is proved using Lyapunov's theorem in both turbine and generator sections. Numerical simulations at the end of the paper confirm the correctness of the designed control approached
[1]H. Fathabadi. Optimal control of a wind energy conversion system and a wind turbine. Optimal control and Application methods.2018. 39(4):1354-1370. 10.1002/oca.2415
[2] H. Gouabi, A. Hazzab, M. Habbab, M. Rezkallah and A. Chandra: Experimental implementation of a novel scheduling algorithm for adaptive and modified P&O MPPT controller using fuzzy logic for WECS. International Journal of Adaptive Control and Signal Processing. 2021.35(9):1732-1753.doi.org/10.1002/acs.3288
[3] S. M. Mehdizadeh Moghadam, E. Alibeiki1 and A. Khosravi .Modelling and Control of 6MG Siemens Wind Turbine Blades Angle and Rotor Speed. International Journal on Electrical Engineering and Informatics.2019.22(2):80-100.10.15676/ijeei.2019.11.1.5
[4] T.Samina, S.Ramalyer, A.B.Beevi . Dynamic behavior of wind driven doubly fed induction generator with rotor side control for wind power application. In:2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS),Chennai, India, 01-02 August 2017 ,pp. 2470-2475.10.1109/ICECDS.2017.8389896.
[5] X. Zhang, H. Liu, Y. Fu and Y. Li. Virtual Shaft Control of DFIG-Based Wind Turbines for Power Oscillation Suppression. IEEE Transactions on Sustainable Energy. 2022. 13(4): 2316-2330. 10.1109/TSTE.2022.3194164
[6] El. Mourabit Y, Derouich A, Allouhi A, El Ghzizal A, El Ouanjli N, Zamzoumyes. Sustainable production of wind energy in the main Morocco's sites using permanent magnet synchronous generators. International Transaction Electrical Energy Systems.2020.30(6):12390.10.1002/2050-7038.12390
[7] L.Ouada, S.Benaggoune and S.Belkacem. Neuro- Fuzzy sliding mode controller based on brushless doubly fed induction generator , International Journal of Engineering TRANSACTIONS B: Applications. 2020.33(2): 248-256. 10.5829/IJE.2020.33.02B.09.
[8] A. D., Bebars, A.A. Eladl, G. M. Abdulsalam . Internal electrical fault detection techniques in DFIG-based wind turbines: a review. Protection and Control of Modern Power Systems.2022.7(18):236-251.10.1186/s41601-022-00236-z.
[9] J. Shair, X. Xie, J. Yang, J. Li and H. Li . Adaptive Damping Control of Sub synchronous Oscillation in DFIG-Based Wind Farms Connected to Series-Compensated Network. IEEE Transactions on Power Delivery 2022.37(2):1036-1049.10.1109/TPWRD.2021.3076053.
[10] M.J.Morshed.A.Fekin.Design of a chattering-free integral terminal sliding mode approach for DFIG-based wind energy system, Optimal control and Application methods.2020.41(5): 1718-1734.10.1002/oca.2635.
[11] LN Huang,B yang,Xs Zhang,Lf Yin,TY u,ZH Fang .Optimal power tracking of doubly fed induction generator-based wind turbine using swarm moth-flame optimizer. Transactions of the Institute of Measurement and Control.2017.41(6):1491-1503.10.1177/014233121771209.
[12] D.Yu,Y.Mao,B.Gu,S.Nojavan,M.Nasseri.A new LQG optimal control strategy applied on a hybrid wind turbine solid oxide fuel cell. In the present of the interval uncertainties. Sustainable Energy, Grids and Network.2021. 21(4):100-296.10.1016/j.segan.2019.100296.
[13] P. Li, J. Wang , L. Xiong, M. Ma, M. W Khan, and S. Huang.Robust finite-time controller for damping of subsynchronous resonance in doubly fed induction generator wind farm. Journal of Vibration and Control.2020.27(1-2):129-139.10.1177/1077546320924490.
[14] P. P. Pradhan, B. Subudhi and A. Ghosh. A Robust Multiloop Disturbance Rejection Controller for a Doubly Fed Induction Generator-Based Wind Energy Conversion System .in IEEE Journal of Emerging and Selected Topics in Power Electronics.2022.10(5):6266-6273. 10.1109/JESTPE.2022.3155561.
[15]S.Hamideh Sedigh,Ziyabari , M. AliyariShoorehdeli,and M. Karimirad.Robust fault estimation of a blade pitch and drivetrain system in wind turbine model. Journal of Vibration and Control.2021.27(3-4)277-294.10.1177/1077546320926274.
[16] A.Asgharnia,R.Shahnazi,A. Jamali .Performance and robustness of optimal fractional fuzzy PID controller for pitch controller of a wind turbine using chaotic optimization algorithms , ISA Transaction .2018.79(6):27-44.10.1016/j.isatra.2018.04.016.
[17] B. Bossoufi. Rooted Tree Optimization for the Backstepping Power Control of a Doubly Fed Induction Generator Wind Turbine., in IEEE Access. 2021.9(2):26512-26522.10.1109/ACCESS.2021.3057123.
[18] Sh. G. Karad and R. Thakur. Enhanced control of doubly fed induction generator based wind turbine system using soft computing assisted fractional order controller. Renewable Energy Focus2022.43(2):291-308.10.1016/j.ref.2022.10.006.
[19] Bossanyi E, Fleming P, Wright A.Validation of individual pitch control by field tests on two- and three-bladed wind turbines. IEEE Transaction Control System Technology.2014.21(4):1067-78 . 10.1109/TCST.2013.2258345.
[20] J. E. Sierra-García and M. Santos .Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators, IEEE Access. 2021. 9: 10413-10425 .10.1109/ACCESS.2021.3051063.
[21] R. Prasad and N. P. Padhy.Synergistic Frequency Regulation Control Mechanism for DFIG Wind Turbines with Optimal Pitch Dynamics. IEEE Transactions on Power Systems. 2020.35(4): 3181.3191. 10.1109/TPWRS.2020.2967468
[22] H. Chojaa, Derouich, A., Taoussi, M., Zamzoum, O., Yessef, M. Optimization of DFIG wind turbine power quality through adaptive fuzzy control, Digital Technologies and Applications.in: ICDTA 2021. In: Lecture Notes in Networks and Systems, 211, Springer. 2021.pp.235-1244.10.1007/978-3-030-73882-2_113.
[23] L. Djilali, A. Badillo-Olvera, Y. Yuliana Rios, H. López-Beltrán and L. Saihi .Neural High Order Sliding Mode Control for Doubly Fed Induction Generator based Wind Turbines. IEEE Latin America Transactions. 2022.20(2): 223-232.10.1109/TLA.2022.9661461.
[24] S. M. Mehdizadeh Moghadam, A. R. Khosravi and S. M. Rakhtala Rostami. Design of a Robust Sliding Mode Controller based on Nonlinear Modeling of Variable Speed Wind Turbine. Majlesi Journal of Electrical Engineering. 2017.11(4): 1-9.10.30486/mjee.2024.2000004.1301.
[25] Ali Karami-Mollaee,Ali Asgghar shojaei,Oscar Baram bones,Mohd Fauziothman .Dynamic sliding mode control of pitch blade wind turbine using sliding mode observer ,Transactions of the Institute of Measurement and Control. 2022. 44(10): 1491-1503. 10.1177/01423312221099304.
[26] K. Tahir, T. Allaoui, M. Denai, et al .Second-order sliding mode control of wind turbines to enhance the fault-ride through capability under unbalanced grid faults, International Journal of Adaptive Control and Signal Processing.2021.49(7): 1959-1986.10.1002/cta.3023.
[27] Aghiles Ardjal ,Rachid Mansouri,Maamar Bettayeb . Fractional sliding mod control of wind turbine for maximum power point tracking , Transactions of the Institute of Measurement and Control.2019.41(2):447-457.10.1177/01423312187645.
[28] L. Xiong, Wang, J., Mi, X., Khan, M.W . Fractional order sliding mode based direct power control of grid-connected DFIG. IEEE Transaction Power System. 2018.33 (3): 3087-3096.10.1109/TPWRS.2017.2761815.
[29] H. Zhang and T. Wang. Finite-Time Sliding Mode Control for Uncertain Neutral Systems With Time Delays, IEEE Access. 2021.9: 140446-140455. 10.1109/ACCESS.2021.3119628.
[30] Z.Yan,S.Zhong,S.He. Finite-time H2/Hinf control for linear Ito stochastic Markovian jumper systems with Brownian motion and Poisson jumper, System and Control Letters.2022.165(4):105285.10.1016/j.sysconle.2022.105285.
[31] Patnaik, R.K., Dash, P.K. & Mishra, S.P. Adaptive third order terminal sliding mode power control of DFIG based wind farm for power system stabilization, International Journal of Dynamics and Control.2020. 8(4):629–643.10.1007/s40435-019-00567-0.
[32] M. J. Morshed and A. Fekih . Second order integral terminal sliding mode control for voltage sag mitigation in DFIG-based wind turbines. In: IEEE Conference on Control Technology and Applications (CCTA),Maui,USA,2017. 27-30 August,pp.1724-1736.10.1109/CCTA.2017.8062530.
[35]S.E. Chehaidia, H. Kherfane , H. Cherif , B. Boukhezzar, L. Kadi,H.Chojaa , A.Abderrezak .Robust Nonlinear Terminal Integral Sliding Mode Torque Control for Wind Turbines Considering Uncertainties. IFAC-PapersOnLine. 2022.55(12):228-233.10.1016/j.ifacol.2022.07.316.
[34] M. Ali, S. M. Amrr and M. Khalid. Speed control of a wind turbine–driven doubly fed induction generator using sliding mode technique with practical finite‐time stability, Frontiers in Energy Research.2022. 10(2): 970755 .10.3389/fenrg.2022.970755.
[35] L. Haitao and T. Zhang .Adaptive Neural Network Finite-Time Control for Uncertain Robotic Manipulators, Journal of Intelligent & Robotic Systems. 2014.75(4): 363-377.10.1007/s10846-013-9888-5.
[36] Y. Wang, L. Gu, Y. Xu and X. Cao. Practical Tracking Control of Robot Manipulators with Continuous Fractional-Order Nonsingular Terminal Sliding Mode , IEEE Transactions on industrial electronics, 20156.63(10): 6194-6204.10.1109/TIE.2016.2569454.
[37] Hong,Y., Huang, J., & Xu,Y. On an output finite-time stabilization problem, IEEE Transactions on Automatic Control, 2001.46(2):305-309.10.1109/9.905699.
[38]S. Labdai, N. Bounar , A. Boulkroune , B. Hemici , L. Nezli.Artificial neural network-based adaptive control for a DFIG-based WECS.ISA Transactions.2022. 128(b):171-180.10.1016/j.isatra.2021.11.045.
[39] M. Karim, M. Taoussi, H. Aroussi, M. Bouderbala, S. Motahhir, M. Baïlo Camara.DSPACE-based implementation for observer backstepping power control of DFIG wind turbine. IET Electric Power Application.2020.14(12):2395-2403.10.1049/iet-epa.2020.0364.
[40] J. Ma,Z. Song, Y. Zhang, Yu.Zhao, J S. Thorp.Robust Stochastic Stability Analysis Method of DFIG Integration on Power System Considering Virtual Inertia Control. IEEE Transactions on Power Systems.2017.32(5):4069-4079.10.1109/TPWRS.2017.2657650.
[41] Y. Yuan, Xu. Chen, J. Tang.Multivariable robust blade pitch control design to reject periodic loads on wind turbines .Renewable Energy.2020.146(2):329-341.10.1016/j.renene.2019.06.136.
[42] O. Zamzoum, El Mourabit, Y. Errouha, M. Derouich, A. El Ghzizal, .A power control of variable speed wind turbine based on doubly fed induction generator using indirect field-oriented control with fuzzy logic controllers for performance optimization. Energy Science Engineering .2018.6 (5): 408-423.10.1002/ese3.215.
[43] M. N. Soorki and M. S. Tavazoei. Adaptive robust control of fractional-order swarm systems in the presence of model uncertainties and external disturbances, IET Control Theory & Applications.2018.12(7):961-969.10.1049/iet-cta.2017.0035.
[44] Si-Ammour, A, Denounce, S., Bettayeb, M. A sliding mode control for linear fractional systems with input and state delays, Communications in Nonlinear Science and Numerical Simulation .2018.14(5):2310–2318.10.1016/j.cnsns.2008.05.011.
[45] Y. Mi, Y. Song, Y. Fu, X. Su, C. Wang and J. Wang. Frequency and Voltage Coordinated Control for Isolated Wind–Diesel Power System Based on Adaptive Sliding Mode and Disturbance Observer, in IEEE Transactions on Sustainable Energy. 2019.10(4):2075-2083.10.1109/TSTE.2018.2878470.
