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access icon free Integrated optimal active and reactive power control scheme for grid connected permanent magnet synchronous generator wind turbines

© The Institution of Engineering and Technology
Received 03/09/2017, Accepted 31/12/2017, Revised 15/12/2017, Published 02/01/2018

This research presents a novel approach for optimal active and reactive power control using permanent magnet synchronous generator with fully rated converter (PMSG-FRC). The power output variations and wind energy intermittent performance create significant challenges for power system integration, operation, and control. A fast and reliable sensorless optimal wind speed (WS) control system-based extreme learning machines and complete ensemble empirical mode decomposition with adaptive noise is developed. Also, an approximate entropy-based complexity measure is used for online WS estimation. The fuzzy controller system is used for calculating the optimal wind turbine (WT) speed, power, and torque, and then adjusts WT by the optimal speed value. Moreover, the wind farm (WF) active and reactive power controller is utilised for obtaining the active and reactive power reference values for every region/feeder. The system is applied and integrated to power system grid by using IEEE standard models, and the speed of WT is adjusted by the required active power level and the estimated WS value to increase the maximum power capture from WF. The simulations results using different cases studies and power system voltage levels have confirmed the validity and accuracy of the proposed control algorithms for practical applications and demonstrated excellent performance for power system integration.

Inspec keywords: synchronous generators; wind power plants; entropy; IEEE standards; reactive power control; permanent magnet generators; learning (artificial intelligence); Hilbert transforms; angular velocity control; power generation control; maximum power point trackers; fuzzy control; approximation theory; control engineering computing; sensorless machine control; power grids; optimal control; wind turbines

Other keywords: WS control system-based extreme learning machines; entropy-based complexity measure approximation; power system integration; fuzzy controller system; wind farm reactive power control scheme; MATLAB platforms; grid connected permanent magnet synchronous generator wind turbine; power system control; IEEE standard models; optimal WT power calculation; wind farm active power control scheme; FRC; wind energy intermittent performance; power system voltage levels; sensorless optimal wind speed control system-based extreme learning machines; power system operation; adaptive noise; integrated optimal active and reactive power control scheme; real WS data; Australia; hybrid maximum power tracking system; power system grid; optimal WT torque calculation; optimal wind turbine speed calculation; WF; intelligent DIgSILENT platforms; fully rated converter; complete ensemble empirical mode decomposition; online WS estimation

Subjects: Velocity, acceleration and rotation control; Wind power plants; Interpolation and function approximation (numerical analysis); Synchronous machines; Power and energy control; Knowledge engineering techniques; DC-DC power convertors; Fuzzy control; Integral transforms in numerical analysis; Interpolation and function approximation (numerical analysis); Integral transforms in numerical analysis; Optimal control; Control of electric power systems; Control engineering computing

References

    1. 1)
      • 44. Yin, M., Xu, Y., Shen, C., et al: ‘Turbine stability-constrained available wind power of variable speed wind turbines for active power control’, IEEE Trans. Power Syst., 2016, PP, (99), p. 1.
    2. 2)
      • 37. Yuan, X., Li, Y.: ‘Control of variable pitch and variable speed direct-drive wind turbines in weak grid systems with active power balance’, IET Renew. Power Gener., 2014, 8, (2), pp. 119–131.
    3. 3)
      • 20. Zhao, H., Wu, Q., Wang, J., et al: ‘Combined active and reactive power control of wind farms based on model predictive control’, IEEE Trans. Energy Convers., 2017, PP, (99), p. 1.
    4. 4)
      • 29. Köller, J., Köppel, J., Peters, W.: ‘Offshore wind energy: research on environmental impacts’ (Springer Science and Business Media, Springer Berlin Heidelberg New York, 2006).
    5. 5)
      • 40. DIgSILENT: ‘PowerFactory User's Manual 15.0. DIgSILENT GmbH Technol’, 2015.
    6. 6)
      • 9. Gonzalez-Longatt, F., Rueda, J.L., Bogdanov, D.: ‘Probabilistic assessment of operational risk considering different wind turbine technologies’. 2012 3rd IEEE PES Int. Conf. Exhibition Innovative Smart Grid Technologies (ISGT Europe), 2012, pp. 1–6.
    7. 7)
      • 11. Sørensen, A.D.H.P., Iov, F., Blaabjerg, F., et al: ‘Wind farm models and control strategies’. Technical Report, Risø National Laboratory, Risø-R-1464(EN), 2005.
    8. 8)
      • 19. Muyeen, S.M., Takahashi, R., Murata, T., et al: ‘A variable speed wind turbine control strategy to meet wind farm grid code requirements’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 331–340.
    9. 9)
      • 51. Revel, G., Leon, A.E., Alonso, D.M., et al: ‘Dynamics and stability analysis of a power system with a PMSG-based wind farm performing ancillary services’, IEEE Trans. Circuits Syst. I Regul. Pap., 2014, 61, (7), pp. 2182–2193.
    10. 10)
      • 14. Wan, C., Lin, J., Wang, J., et al: ‘Direct quantile regression for nonparametric probabilistic forecasting of wind power generation’, IEEE Trans. Power Syst., 2016, PP, (99), p. 1.
    11. 11)
      • 24. Raju, S.K., Pillai, G.N.: ‘Design and implementation of type-2 fuzzy logic controller for DFIG-based wind energy systems in distribution networks’, IEEE Trans. Sustain. Energy, 2016, 7, (1), pp. 345–353.
    12. 12)
      • 35. Singh, M., Chandra, A.: ‘Application of adaptive network-based fuzzy inference system for sensorless control of PMSG-based wind turbine with nonlinear-load-compensation capabilities’, IEEE Trans. Power Electron., 2011, 26, (1), pp. 165–175.
    13. 13)
      • 2. Morales, A., Robe, X., Sala, M., et al: ‘Advanced grid requirements for the integration of wind farms into the Spanish transmission system’, IET Renew. Power Gener., 2008, 2, (1), pp. 47–59.
    14. 14)
      • 28. Sun, D., Wang, X., Nian, H., et al: ‘A sliding-mode direct power control strategy for DFIG under both balanced and unbalanced grid conditions using extended active power’, IEEE Trans. Power Electron., 2018, 33, (2), pp. 1313–1322.
    15. 15)
      • 33. Wei, Q., Xu, Y., Xiang, G.: ‘Wind speed and rotor position sensorless control for direct-drive PMG wind turbines’, IEEE Trans. Ind. Appl., 2012, 48, (1), pp. 3–11.
    16. 16)
      • 7. Chi, S., Weihao, H., Zhe, C., et al: ‘Mitigation of power system oscillation caused by wind power fluctuation’, Renew. Power Gener., IET, 2013, 7, (6), pp. 639–651.
    17. 17)
      • 32. Galdi, V., Piccolo, A., Siano, P.: ‘Designing an adaptive fuzzy controller for maximum wind energy extraction’, IEEE Trans. Energy Convers., 2008, 23, (2), pp. 559–569.
    18. 18)
      • 10. Rueda, J.L., Erlich, I.: ‘Impacts of large scale integration of wind power on power system small-signal stability’. 2011 4th Int. Conf. Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011, pp. 673–681.
    19. 19)
      • 25. Dongkyoung, C., Kyo-Beum, L.: ‘Variable structure control of the active and reactive powers for a DFIG in wind turbines’, Ind. Appl., IEEE Trans., 2010, 46, (6), pp. 2545–2555.
    20. 20)
      • 41. Stifter, M., Schwalbe, R., Andren, F., et al: ‘Steady-state co-simulation with PowerFactory’. 2013 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES), 2013, pp. 1–6.
    21. 21)
      • 21. Qian, M.-h., Zhao, D.-w., Jiang, D.-j., et al: ‘Active and reactive power control of wind farm based on integrated platform of PowerFactory and MATLAB’. inGonzalez-Longatt, F.M., Luis Rueda, J., (Eds): ‘PowerFactory applications for power system analysis’ (Springer International Publishing, Cham, 2014), pp. 421–446.
    22. 22)
      • 52. Alepuz, S., Calle, A., Busquets-Monge, S., et al: ‘Use of stored energy in PMSG rotor inertia for low-voltage ride-through in back-to-back NPC converter-based wind power systems’, IEEE Trans. Ind. Electron., 2013, 60, (5), pp. 1787–1796.
    23. 23)
      • 26. Tazil, M., Kumar, V., Bansal, R.C., et al: ‘Three-phase doubly fed induction generators: an overview’, IET Electr. Power Appl., 2010, 4, (2), pp. 75–89.
    24. 24)
      • 13. Le-Ren, C.-C., Chih-Che, S., Yu-Ju, Y.: ‘Modeling of wind farm participation in AGC’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 1204–1211.
    25. 25)
      • 18. Pham, H.V., Rueda, J.L., Erlich, I.: ‘Online optimal control of reactive sources in wind power plants’, IEEE Trans. Sustain. Energy, 2014, 5, (2), pp. 608–616.
    26. 26)
      • 50. Zeng, X., Yao, J., Chen, Z., et al: ‘Co-ordinated control strategy for hybrid wind farms with PMSG and FSIG under unbalanced grid voltage condition’, IEEE Trans. Sustain. Energy, 2016, 7, (3), pp. 1100–1110.
    27. 27)
      • 42. Baccino, F., Conte, F., Grillo, S., et al: ‘An optimal model-based control technique to improve wind farm participation to frequency regulation’, IEEE Trans. Sustain. Energy, 2015, 6, (3), pp. 993–1003.
    28. 28)
      • 36. Kanellos, F.D., Hatziargyriou, N.D.: ‘Control of variable speed wind turbines equipped with synchronous or doubly fed induction generators supplying islanded power systems’, IET Renew. Power Gener., 2009, 3, (1), pp. 96–108.
    29. 29)
      • 17. Leon, A.E., Revel, G., Alonso, D.M., et al: ‘Wind power converters improving the power system stability’, IET Gener. Transm. Distrib., 2016, 10, (7), pp. 1622–1633.
    30. 30)
      • 6. Gasch, R., Twele, J.: ‘Wind power plants: fundamentals, design, construction and operation’ (Springer Science and Business Media, Springer Heidelberg Dordrecht London New York, 2011).
    31. 31)
      • 4. Meng, K., Zhang, W., Dong, Z.Y., et al: ‘Hierarchical SCOPF considering wind energy integration through multi-terminal VSC-HVDC grids’, IEEE Trans. Power Syst., 2017, PP, (99), pp. 1–1.
    32. 32)
      • 49. Redondo, K., Gutierrez, J.J., Saiz, P., et al: ‘Accurate differentiation for improving the flicker measurement in wind turbines’, IEEE Trans. Power Deliv., 2017, 32, (1), pp. 88–96.
    33. 33)
      • 8. Suriyaarachchi, D.H.R., Annakkage, U.D., Karawita, C., et al: ‘A procedure to study Sub-synchronous interactions in wind integrated power systems’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 377–384.
    34. 34)
      • 39. Kerahroudi, S.K., Alamuti, M.M., Li, F., et al: ‘Application and requirement of DIgSILENT PowerFactory to MATLAB/simulink interface’, in Gonzalez-Longatt, F.M., Luis Rueda, J. (Eds): ‘Powerfactory applications for power system analysis’ (Springer International Publishing, Cham, 2014), pp. 297–322.
    35. 35)
      • 27. Phan, V.-T., Lee, H.-H.: ‘Improved predictive current control for unbalanced stand-alone doubly-fed induction generator-based wind power systems’, IET Electr. Power Appl., 2011, 5, (3), pp. 275–287.
    36. 36)
      • 16. Pardalos, P.M., Rebennack, S., Pereira, M.V., et al: ‘Handbook of wind power systems’ (Springer, Springer-Verlag Berlin Heidelberg, 2013).
    37. 37)
      • 30. Hu, K.-W., Liaw, C.-M.: ‘Position sensorless surface-mounted permanent-magnet synchronous generator and its application to power DC microgrid’, IET Power Electron., 2015, 8, (9), pp. 1636–1650.
    38. 38)
      • 53. Nasiri, M., Mohammadi, R.: ‘Peak current limitation for grid side inverter by limited active power in PMSG-based wind turbines during different grid faults’, IEEE Trans. Sustain. Energy, 2017, 8, (1), pp. 3–12.
    39. 39)
      • 45. Rather, Z.H., Chen, Z., Thøgersen, P., et al: ‘Dynamic reactive power compensation of large-scale wind integrated power system’, IEEE Trans. Power Syst., 2015, 30, (5), pp. 2516–2526.
    40. 40)
      • 5. Xia, C., Haishun, S., Jinyu, W., et al: ‘Integrating wind farm to the grid using hybrid multiterminal HVDC technology’, IEEE Trans. Ind. Appl., 2011, 47, (2), pp. 965–972.
    41. 41)
      • 23. Mahmoud, T., Dong, Z.Y., Ma, J.: ‘A developed integrated scheme based approach for wind turbine intelligent control’, IEEE Trans. Sustain. Energy, 2016, PP, (99), p. 1.
    42. 42)
      • 12. Kayikci, M., Milanovic, J.V.: ‘Reactive power control strategies for DFIG-based plants’, IEEE Trans. Energy Convers., 2007, 22, (2), pp. 389–396.
    43. 43)
      • 15. Sørensen, P., Hansen, A.D., Thomsen, K., et al: ‘Operation and control of large wind turbines and wind farms’, Energy, 2005, p. 44Report No Ris-R-1532.
    44. 44)
      • 22. Kasem, A.H., El-Saadany, E.F., El-Tamaly, H.H., et al: ‘Power ramp rate control and flicker mitigation for directly grid connected wind turbines’, IET Renew. Power Gener., 2010, 4, (3), pp. 261–271.
    45. 45)
      • 47. Zhang, F., Meng, K., Dong, Z., et al: ‘Battery ESS planning for wind smoothing via variable-interval reference modulation and self-adaptive SOC control strategy’, IEEE Trans. Sustain. Energy, 2017, 8, (2), pp. 695–707.
    46. 46)
      • 1. Siemes, P., Haubrich, H.J., Vennegeerts, H., et al: ‘Concepts for the improved integration of wind power into the German interconnected system’, IET Renew. Power Gener., 2008, 2, (1), pp. 26–33.
    47. 47)
      • 43. MacDowell, J., Dutta, S., Richwine, M., et al: ‘Serving the future: advanced wind generation technology supports ancillary services’, IEEE Power Energy Mag., 2015, 13, (6), pp. 22–30.
    48. 48)
      • 3. Leao, R.P.S., Antunes, F.L.M., Lourenco, T.G.M., et al: ‘A comprehensive overview on wind power integration to the power grid’, IEEE Latin Am. Trans., 2009, 7, (6), pp. 620–629.
    49. 49)
      • 46. Malik, H., Mishra, S.: ‘Artificial neural network and empirical mode decomposition based imbalance fault diagnosis of wind turbine using TurbSim, FAST and simulink’, IET Renew. Power Gener., 2016, 11, (6), pp. 889–902.
    50. 50)
      • 38. Stifter, M., Andrén, F., Schwalbe, R., et al: ‘Interfacing PowerFactory: co-simulation, real-time simulation and controller hardware-in-the-loop applications’ (Springer, 2014), pp. 343–366.
    51. 51)
      • 48. Rijcke, S.D., Ergun, H., Hertem, D.V., et al: ‘Grid impact of voltage control and reactive power support by wind turbines equipped with direct-drive synchronous machines’, IEEE Trans. Sustain. Energy, 2012, 3, (4), pp. 890–898.
    52. 52)
      • 34. Hui, L., Shi, K.L., McLaren, P.G.: ‘Neural-network-based sensorless maximum wind energy capture with compensated power coefficient’, IEEE Trans. Ind. Appl., 2005, 41, (6), pp. 1548–1556.
    53. 53)
      • 31. Yaramasu, V., Dekka, A., Durán, M.J., et al: ‘PMSG-based wind energy conversion systems: survey on power converters and controls’, IET Electr. Power Appl., 2017, 11, (6), pp. 956–968.
    54. 54)
      • 54. Bubshait, A.S., Mortezaei, A., Simões, M.G., et al: ‘Power quality enhancement for a grid connected wind turbine energy system’, IEEE Trans. Ind. Appl., 2017, 53, (3), pp. 2495–2505.
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