Super-Twisting Control for a Doubly Fed Induction Generator (DFIG)-Based Wind Turbine Using a Nonlinear Observer

Document Type : Research paper

Authors

1 AVCIS Laboratory, Automation Department, University of Science and Technology Mohamed Boudiaf, El Mnaouar, BP 1505, Bir El Djir 31000, Oran, Algeria

2 LAAS laboratory, Electrical Engineering Department, Polytechnic School Maurice Audin of Oran, El Mnaouer, BP 1523 Oran, Algeria

Abstract

The high cost and complexity of using sensors for controlling processes have led to the development of observer techniques that aim to estimate system states without the need for sensors. These techniques reduce system complexity and can potentially reduce product and maintenance costs. In this paper, we present an interconnected high gain observer (IHGO) that estimates the electromagnetic torque, speed, and position of a doubly fed induction generator-based wind turbine (DFIG-WT) using only voltage, current, and wind speed measurements. The IHGO is designed to be robust to parameter uncertainties and its stability is assessed using Lyapunov theory. To guarantee finite time convergence, a Super Twisting-based High Order Sliding Mode (ST-HOSM) controller is used for direct torque control. The ST-HOSM is a simple algorithm that maintains the sliding mode characteristics, provides robustness against disturbance, and reduces the chattering phenomenon. The controller and observer are designed in the $\alpha\beta$ frame to avoid the use of a phase-locked loop (PLL). Simulation results confirmed the effectiveness of the proposed control strategy under parameter uncertainties, power and speed variations, grid voltage dip and current sensor noise.

Keywords

References

  1. World Wind Energy Association and others,“Worldwide Wind Capacity Reaches 744 Gigawatts – An Unprecedented 93 Gigawatts added in 2020,” https://wwindea.org/worldwidewind-capacity-reaches-744-gigawatts/ (accessed Dec. 12, 2021).
  2. “Vestas–Page 16.” https://nozebra.ipapercms.dk/Vestas/Communication/Productbrochure/enventus/enventus/enventusbrochure-2021/?page=16 (accessed Dec. 12, 2021).
  3. “Vestas.” https://nozebra.ipapercms.dk/Vestas/Communication/Productbrochure/OffshoreProductBrochure/v236-150mwbrochure/?page=1 (accessed Dec. 12, 2021).
  4. Faramarzi, S. Abazari, S. Hoghoughi, and N.R. Abjadi, “Improved Power System Dynamic Stability by DFIG in the Presence of SSSC Using Adaptive Nonlinear Multi-Input Backstepping,” J. Oper. Autom. Power Eng., Oct. 2022, doi: 10.22098/JOAPE.2023.10565.1753.
  5. Nikpayam, M. Ghanbari, A. Esmaeli, and M. Jannati, "Vector Control Methods for Star-Connected Three-Phase Induction Motor Drives Under the Open-Phase Failure," J. Oper. Autom. Power Eng., vol. 10, no. 2, pp. 155-164, Aug. 2022, doi: 10.22098/JOAPE.2022.8802.1616.
  6. Dahmardeh, M. Ghanbari, and S.M. Rakhtala, “A Novel Combined DTC Method and SFOC System for Three-phase Induction Machine Drives with PWM Switching Method,” J. Oper. Autom. Power Eng., vol. 11, no. 2, pp. 76-–82, Aug. 2023, doi: 10.22098/JOAPE.2023.9717.1679.
  7. Hasanzadeh, H. Shayeghi, and S.R. Mousavi-Aghdam, "A New Fuzzy Direct Power Control of Doubly-Fed Induction Generator in a Wind Power System," J. Oper. Autom. Power Eng., vol. 10, no. 3, pp. 179-188, Dec. 2022, doi: 10.22098/JOAPE.2022.7662.1545.
  8. Karad and R. Thakur, “Recent Trends of Control Strategies for Doubly Fed Induction Generator Based Wind Turbine Systems: A Comparative Review,” Arch. Comput. Methods Eng., vol. 28, no. 1, pp. 15-29, Jan. 2021, doi: 10.1007/s11831-019-09367-3.
  9. Dejamkhooy and A. Ahmadpour, “Torque Ripple Reduction of the Position Sensor-less Switched Reluctance Motors Applied in the Electrical Vehicles,” J. Oper. Autom. Power Eng., vol. 11, no. 4, 2022, doi: 10.22098/JOAPE.2022.9908.1694.
  10. Mohamed, B. Abdelmadjid, and B. Lotfi, “Improvement of Direct Torque Control Performances for Induction Machine Using a Robust Backstepping Controller and a New Stator Resistance Compensator,” Eur. J. Electr. Eng., vol. 22, no. 2, pp. 137-144, Apr. 2020, doi: 10.18280/EJEE.220207.
  11. Utkin, A. Poznyak, Y. Orlov, and A. Polyakov, “Conventional and high order sliding mode control,” J. Franklin Inst., vol. 357, no. 15, pp. 10244–10261, Oct. 2020, doi: 10.1016/J.JFRANKLIN.2020.06.018.
  12. Liao, Y. Hao, T. Guo, B. Lv, and Q. Wang, “Second-Order Sliding Mode Control of Permanent Magnet Synchronous Motor Based on Singular Perturbation,” Energies, vol. 15, no. 21, p. 8028, Oct. 2022, doi: 10.3390/EN15218028.
  13. Kaplan and F. Bodur, “Second-order sliding mode controller design of buck converter with constant power load,” Int. J. Control, 2022, doi: 10.1080/00207179.2022.2037718.
  14. Matraji, K. Al-Wahedi, and A. Al-Durra, “Higher-Order Super-Twisting Control for Trajectory Tracking Control of Skid-Steered Mobile Robot,” IEEE Access, vol. 8, pp. 124712-–124721, 2020, doi: 10.1109/ACCESS.2020.3007784.
  15. Levant, “Higher-order sliding modes, differentiation and output-feedback control,” Int. J. Control, vol. 76, no. 9-10, pp. 924-941, May 2003, doi: 10.1080/0020717031000099029.
  16. Tahir, T. Allaoui, M. Denai, S. Mekhilef, C. Belfedal, and M. Doumi, “Second-order sliding mode control of wind turbines to enhance the fault-ride through capability under unbalanced grid faults,” Int. J. Circuit Theory Appl., vol. 49, no. 7, pp. 1959-1986, Jul. 2021, doi: 10.1002/CTA.3023.
  17. Celik, H. Ahmed, and M.E. Meral, “Kalman FilterBased Super-Twisting Sliding Mode Control of Shunt Active Power Filter for Electric Vehicle Charging Station Applications,” IEEE Trans. Power Deliv., 2022, doi: 10.1109/TPWRD.2022.3206267.
  18. V. Hollweg, P.J.D. de Oliveira Evald, R.V. Tambara, and H.A. Gründling, “A Robust Adaptive Super-Twisting Sliding Mode Controller applied on grid-tied power converter with an LCL filter,” Control Eng. Pract., vol. 122, p. 105104, May 2022, doi: 10.1016/J.CONENGPRAC.2022.105104.
  19. M. Rakhtala and A. Casavola, “Real-Time Voltage Control Based on a Cascaded Super Twisting Algorithm Structure for DC-DC Converters,” IEEE Trans. Ind. Electron., vol. 69, no. 1, pp. 633-641, Jan. 2022, doi: 10.1109/TIE.2021.3051551.
  20. M. Alhato, S. Bouallègue, and H. Rezk, “Modeling and Performance Improvement of Direct Power Control of Doubly-Fed Induction Generator Based Wind Turbine through Second-Order Sliding Mode Control Approach,” Math. 2020, vol. 8, no. 11, p. 2012, Nov. 2020, doi: 10.3390/MATH8112012.
  21. Kelkoul and A. Boumediene, “Stability analysis and study between classical sliding mode control (SMC) and super twisting algorithm (STA) for doubly fed induction generator (DFIG) under wind turbine,” Energy, vol. 214, p. 118871, Jan. 2021, doi: 10.1016/J.ENERGY.2020.118871.
  22. Dekali, L. Baghli, and A. Boumediene, “Improved Super Twisting Based High Order Direct Power Sliding Mode Control of a Connected DFIG Variable Speed Wind Turbine,” Period. Polytech. Electr. Eng. Comput. Sci., vol. 65, no. 4, pp. 352-372, Oct. 2021, doi: 10.3311/PPEE.17989.
  23. Q. Pei, H.B. Gu, K.X. Liu, and J.H. Lü, “An overview on the designs of distributed observers in LTI multi-agent systems,” Sci. China Technol. Sci., vol. 64, no. 11, pp. 2337-–2346, 2021, doi: 10.1007/s11431-020-1790-3.
  24. Gholipour, M. Ghanbari, E. Alibeiki, and M. Jannati, “Sensorless FOC Strategy for Current Sensor Faults in Three-Phase Induction Motor Drives,” J. Oper. Autom. Power Eng., vol. 11, no. 1, pp. 1-10, Apr. 2023, doi: 10.22098/JOAPE.2022.9274.1648.
  25. J. Arand, “Optimization of PM Segments Shift Angles for Minimizing the Cogging Torque of YASA-AFPM Machines Using Response Surface Methodology,” J. Oper. Autom. Power Eng., vol. 9, no. 3, pp. 203-212, 2021, doi: 10.22098/joape.2021.7648.1542.
  26. H. Liu, J. Nie, H.L. Wei, L. Chen, X.H. Li, and M.Y. Lv, “Switched PI Control Based MRAS for Sensorless Control of PMSM Drives Using Fuzzy-Logic-Controller,” IEEE Open J. Power Electron., vol. 3, pp. 368-–381, 2022, doi: 10.1109/OJPEL.2022.3182053.
  27. Yan and M. Cheng, “An MRAS Observer-Based Speed Sensorless Control Method for Dual-Cage Rotor Brushless Doubly Fed Induction Generator,” IEEE Trans. Power Electron., vol. 37, no. 10, pp. 12705-12714, Oct. 2022, doi: 10.1109/TPEL.2022.3172362.
  28. Cheng and C. Li, “Luenberger Observer-Based Microgrid Control Strategy for Mixed Load Conditions,” Energies 2022, vol. 15, Page 3655, May 2022, doi: 10.3390/EN15103655.
  29. M. Share Pasand and A.A. Ahmadi, “Performance evaluation and simulation of cubic observers,” ISA Trans., vol. 122, pp. 172-181, Mar. 2022, doi: 10.1016/J.ISATRA.2021.04.032.
  30. H. Al-Bayati, “Determination of Model, Implement and Compare New Two Optimal Adaptive Fault Diagnosis Observers with Six Observers,” Nov. Res. Asp. Math. Comput. Sci., vol. 1, pp. 48-72, Apr. 2022, doi: 10.9734/BPI/NRAMCS/V1/3115E.
  31. S. Tabatabaei, M. Tavakoli, and H.A. Talebi, “A finite-time adaptive order estimation approach for non-integer order nonlinear systems,” ISA Trans., vol. 127, pp. 383-394, Aug. 2022, doi: 10.1016/J.ISATRA.2021.08.034.
  32. Mao, C. Ma, X. Zhang, and W. Liu, “Comparison of Nonlinear Observers for the Back Electromotive Force of the Main Exciter of the Brushless Synchronous Starter/Generator,” 2022 25th Int. Conf. Electr. Mach. Syst., pp. 1-5, Nov. 2022, doi: 10.1109/ICEMS56177.2022.9983455.
  33. Kordestani, M. Mousavi, A. Chaibakhsh, M. Orchard, K. Khorasani, and M. Saif, “A New Compressor Failure Prognostic Method using Nonlinear Observers and a Bayesian Algorithm for Heavy-Duty Gas Turbines,” IEEE Sens. J., pp. 1-1, 2023, doi: 10.1109/JSEN.2022.3233585.
  34. H. Rangegowda, J. Valluru, S.C. Patwardhan, and S. Mukhopadhyay, “Simultaneous and sequential state and parameter estimation using receding-horizon nonlinear Kalman filter,” J. Process Control, vol. 109, pp. 13-31, Jan. 2022, doi: 10.1016/J.JPROCONT.2021.11.003.
  35. Chen, L. Zhang, H. Chen, K. Liang, and X. Chen, “A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors,” Comput. Methods Programs Biomed., vol. 200, p. 105797, Mar. 2021, doi: 10.1016/J.CMPB.2020.105797.
  36. V. Satya Sai Chandra and S. Mohapatro, “Active sensor fault tolerant control of bus voltage in standalone low voltage DC microgrid,” Electr. Eng., pp. 1—14, Dec. 2022, doi: 10.1007/S00202-022-01716-Z/FIGURES/21.
  37. Wang, Q. Wang, H. Zhang, and J. Han, “H-Infinity Observer for Vehicle Steering System with Uncertain Parameters and Actuator Fault,” Actuators 2022, vol. 11, no. 2, p. 43, Jan. 2022, doi: 10.3390/ACT11020043.
  38. Shahzad, A.U. Khan, M. Iqbal, A. Saeed, G. Hafeez, et al., “Sensor Fault-Tolerant Control of Microgrid Using Robust Sliding-Mode Observer,” Sensors 2022, vol. 22, no. 7, p. 2524, Mar. 2022, doi: 10.3390/S22072524.
  39. Lin, L. Liu, and D. Liang, “Hybrid Active Flux Observer to Suppress Position Estimation Error for Sensorless IPMSM Drives,” IEEE Trans. Power Electron., vol. 38, no. 1, pp. 872-886, Jan. 2023, doi: 10.1109/TPEL.2022.3203815.
  40. Naifar and G. Boukettaya, “On Observer Design of Systems Based on Renewable Energy,” Stud. Syst. Decis. Control, vol. 410, pp. 135-–176, 2022, doi: 10.1007/978-3030-92731-8_8/FIGURES/30.
  41. Meng, H. Yu, J. Zhang, T. Xu, H. Wu, and K. Yan, “Disturbance Observer-Based Feedback Linearization Control for a Quadruple-Tank Liquid Level System,” ISA Trans., vol. 122, pp. 146-162, Mar. 2022, doi: 10.1016/J.ISATRA.2021.04.021.
  42. Xu, S. Liu, B. Wang, and J. Wang, “Distributed Observer for Affine Nonlinear Systems and its Application on Automated Highway System,” IEEE Trans. Intell. Veh., 2022, doi: 10.1109/TIV.2022.3163773.
  43. Zolfaghari, G.B. Gharehpetian, M. Shafie-khah, and J.P.S. Catalão, “Comprehensive review on the strategies for controlling the interconnection of AC and DC microgrids,” Int. J. Electr. Power Energy Syst., vol. 136, p. 107742, Mar. 2022, doi: 10.1016/J.IJEPES.2021.107742.
  44. Xiao, X. Pan, and T.T.Y. Yang, “Nonlinear backstepping hierarchical control of shake table using high-gain observer,” Earthq. Eng. Struct. Dyn., vol. 51, no. 14, pp. 3347-3366, Nov. 2022, doi: 10.1002/EQE.3726.
  45. S. Haider Khan and S. Kumar Mallik, “Mechanical sensorless control of a rotor-tied DFIG wind energy conversion system using a high gain observer,” J. King Saud Univ. Eng. Sci., 2022, doi: 10.1016/J.JKSUES.2022.05.005.
  46. Lascu and G.D. Andreescu, “PLL position and speed observer with integrated current observer for sensorless PMSM drives,” IEEE Trans. Ind. Electron., vol. 67, no. 7, pp. 5990-5999, Jul. 2020, doi: 10.1109/TIE.2020.2972434.
  47. R. Kafi, M.A. Hamida, H. Chaoui, and R. Belkacemi, “Sliding Mode Self-Sensing Control of Synchronous Machine Using Super Twisting Interconnected Observers,” Energies , vol. 13, no. 16, p. 4199, Aug. 2020, doi: 10.3390/EN13164199.
  48. Zhang, Z.T. Li, Q.M. Wu, B. Dahhou, and M. Cabassud, “Actuator fault diagnose for interconnected system via invertibility,” Int. J. Model. Identif. Control, vol. 33, no. 4, pp. 283-298, 2019, doi: 10.1504/IJMIC.2019.107487.
  49. A. Hamida, J. De Leon-Morales, and A. Messali, “Observer design for nonlinear interconnected systems: experimental tests for self-sensing control of synchronous machine,” Int. J. Adv. Manuf. Technol., vol. 105, no. 1-–4, pp. 1041-–1054, Nov. 2019, doi: 10.1007/S00170-019-04253-5/FIGURES/12.
  50. Wu, “Sensorless Control of PMSM via Extended State Interconnected Observer,” ASCC 2022 - 2022 13th Asian Control Conf. Proc., pp. 979-984, 2022.
  51. P. Schaffarczyk, Introduction to wind turbine aerodynamics, 2020th ed. Springer, 2020.
  52. Sun, S. Yan, B. Cai, Y. Wu, and Z. Zhang, “MPPT Adaptive Controller of DC-based DFIG in Resistances Uncertainty,” Int. J. Control. Autom. Syst., vol. 19, no. 8, pp. 2734-2746, Aug. 2021, doi: 10.1007/S12555-020-0302-3/METRICS.
  53. P. Ganthia and S.K. Barik, “Steady-State and Dynamic Comparative Analysis of PI and Fuzzy Logic Controller in Stator Voltage Oriented Controlled DFIG Fed Wind Energy Conversion System,” J. Inst. Eng. Ser. B, vol. 101, no. 3, pp. 273-286, Jun. 2020, doi: 10.1007/S40031-020-004558/TABLES/4.
  54. Sadeghi, S.M. Madani, S. Member, M. Ataei, M.R.A. Kashkooli, and S. Ademi, “Super - Twisting Sliding Mode Direct Power Control of Brushless Doubly Fed Induction Generator,” IEEE Trans. Ind. Electron., vol. 0046, no. VC, 2018, doi: 10.1109/TIE.2018.2818672.
  55. Abad, J. Lopez, M. Rodriguez, L. Marroyo, and G. Iwanski, Doubly fed induction machine: modeling and control for wind energy generation. John Wiley & Sons, 2011.
Journal of Operation and Automation in Power Engineering

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Available Online from 20 August 2023

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  • Receive Date: 13 October 2022
  • Revise Date: 27 February 2023
  • Accept Date: 08 June 2023

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