access icon openaccess Analysis and tuning methodology of FAPI controllers for maximising the share of grid-connected wind generations

In this study, a novel methodology is proposed for sensitivity-based tuning and analysis of derivative-based fast active power injection (FAPI) controllers in type-4 wind turbine units integrated into a low-inertia power system. The FAPI controller is attached to a power electronic interfaced generation (PEIG) represented by a generic model of wind turbines type 4. It consists of a combination of droop and derivative controllers, which is dependent on the measurement of the frequency. The tuning methodology performs parametric sensitivity to search for the most suitable set of parameters of the attached FAPI that minimises the maximum frequency deviation in the containment period. The FAPI is adjusted to safeguard system stability when increasing the share of PEIG. Since the input signal of the FAPI is the measured frequency, the impact of different values and parameter settings of the phase-locked loop used for the FAPI controller is also investigated. Detailed validation with a full-scaled wind power converter is also provided with a real-time digital simulator testbed. Obtained simulation results using a three-area test system, identify the maximum achievable degree of increase in the share of wind power when a proper combination of wind park locations considering their suggested settings for inertia emulation.

Inspec keywords: power generation faults; power grids; power electronics; wind power plants; power generation control; power convertors; wind turbines; frequency measurement

Other keywords: network frequency; derivative-based fast active power injection controllers; wind park locations; power electronic interfaced generation; type-4 wind turbine units; low-inertia power system; droop controller; parameter settings; FAPI controller; maximum frequency deviation; derivative controller; grid-connected wind generations; attached FAPI; frequency containment period; full-scaled wind power converter; tuning methodology; parametric sensitivity; measured frequency; wind turbines type 4; sensitivity-based tuning

Subjects: Wind power plants; Frequency measurement; Control of electric power systems; Power convertors and power supplies to apparatus

References

    1. 1)
      • 14. Ackermann, T.: ‘Wind power in power systems’, (John Wiley & Sons, Ltd, Chichester, UK, 2005).
    2. 2)
      • 5. Mortazavi, H., Mehrjerdi, H., Saad, M., et al: ‘A monitoring technique for reversed power flow detection with high PV penetration level’, IEEE Trans. Smart Grid, 2015, 6, (5), pp. 22212232.
    3. 3)
      • 18. Zeng, X., Liu, T., Wang, S., et al: ‘Coordinated control of MMC-HVDC system with offshore wind farm for providing emulated inertia support’, IET Renew. Power Gener., 2020, 14, (5), pp. 673683.
    4. 4)
      • 36. Oak Ridge National Laboratory: ‘Frequency control concerns in the North American electric power system’ (Tennessee, USA, 2002). Available at https://info.ornl.gov/sites/publications/Files/Pub57419.pdf.
    5. 5)
      • 11. Yao, W., Lee, K.Y.: ‘A control configuration of wind farm for load-following and frequency support by considering the inertia issue’. IEEE Power and Energy Society General Meeting, San Diego, CA, 2011, pp. 16.
    6. 6)
      • 35. Simulation modelling libraries, RTDS Technologies Inc..
    7. 7)
      • 13. Gonzalez-Longatt, F., Chikuni, E., Rashayi, E.: ‘Effects of the synthetic inertia from wind power on the total system inertia after a frequency disturbance’. IEEE Int. Conf. on Industrial Technology (ICIT), Cape Town, 2013, pp. 826832.
    8. 8)
      • 29. ENTSO-E: ‘Continental Europe operation handbook – appendix 1: load-frequency control and performance’ (Brussels, 2009). Available at https://www.entsoe.eu/fileadmin/user_upload/_library/publications/entsoe/Operation_Handbook/Policy_1_final.pdf.
    9. 9)
      • 30. ENTSO-E: ‘Rate of change of frequency (ROCOF) withstand capability’, Brussels, 2018. Available at https://docstore.entsoe.eu/Documents/Network%20codes%20documents/NC%20RfG/IGD_RoCoF_withstand_capability_final.pdf.
    10. 10)
      • 1. Gomez-Exposito, A., Conejo, A.J., Cañizares, C.: ‘Electric energy systems: analysis and operation’ (CRC Press, Boca Raton, FL, USA, 2008).
    11. 11)
      • 22. Lin, W., Yin, Y.: ‘Enhancing frequency response control by DFIGs in the high wind penetrated power systems’, IEEE Trans. Power Syst., 2011, 26, (2), pp. 710718.
    12. 12)
      • 23. Miao, Z., Member, S., Fan, L., et al: ‘Wind farms with HVdc delivery in inertial response and primary frequency control’, IEEE Trans. Energy Convers., 2010, 25, (4), pp. 11711178.
    13. 13)
      • 12. Rakhshani, E., Rodriguez, P.: ‘Inertia emulation in AC/DC interconnected power’, IEEE Trans. Power Syst., 2017, 32, (5), pp. 33383351.
    14. 14)
      • 33. ‘LRP I RFP backgrounder – connection, March 10, 2015’. Available at http://www.ieso.ca/documents/generation-procurement/lrp/lrp-1-final/LRP-IRFP-Backgrounder-Connection.pdf, accessed 1 April 2016.
    15. 15)
      • 3. Gu, H., Yan, R., Saha, T.K.: ‘Minimum synchronous inertia requirement of renewable power systems’, IEEE Trans. Power Syst., 2018, 33, (2), pp. 15331543.
    16. 16)
      • 20. Xiong, L., Li, P., Wu, F., et al: ‘Stability enhancement of power systems with high DFIG-wind turbine penetration via virtual inertia planning’, IEEE Trans. Power Syst., 2019, 34, (2), pp. 13521361.
    17. 17)
      • 4. Poolla, B.K., Groß, D., Dörfler, F.: ‘Placement and implementation of grid-forming and grid-following virtual inertia and fast frequency response’, IEEE Trans. Power Syst., 2019, 34, (4), pp. 30353046.
    18. 18)
      • 15. Rakhshani, E., Remon, D., Cantarellas, A.M., et al: ‘Virtual synchronous power strategy for multiple power systems’, IEEE Trans. Power Syst., 2017, 32, (3), pp. 16651677.
    19. 19)
      • 37. ENTSO-E: ‘Need for synthetic inertia (SI) for frequency regulation’ (Brussels, 2018). Available at https://docstore.entsoe.eu/Documents/Network%20codes%20documents/NC%20RfG/IGD_Need_for_Synthetic_Inertia_final.pdf.
    20. 20)
      • 7. Engelken, S., Mendonca, A., Fischer, M.: ‘Inertial response with improved variable recovery behaviour provided by type 4 WTs’, IET Renew. Power Gener. Spec., 2017, 11, (3), pp. 195201.
    21. 21)
      • 32. Bossanyi, E.: ‘Generic grid frequency response capability for wind power plant’. EWEA Annual Conf., France, Paris, November 2015.
    22. 22)
      • 24. Eriksson, R., Modig, N., Elkington, K.: ‘Synthetic inertia versus fast frequency response: a definition’, IET Renew. Power Gener., 2018, 12, (5), pp. 507514.
    23. 23)
      • 39. Singh, A.K., Pal, B.C.: ‘Rate of change of frequency estimation for power systems using interpolated DFT and kalman filter’, IEEE Trans. Power Syst., 2019, 34, (4), pp. 11.
    24. 24)
      • 10. Mishra, S., Zarina, P.P.: ‘A novel controller for frequency regulation in a hybrid system with high PV penetration’. 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, 2013, pp. 15.
    25. 25)
      • 9. Ha, F., Abdennour, A.: ‘Optimal use of kinetic energy for the inertial support from variable speed wind turbines’, Renew. Energy, 2015, 80, pp. 629643.
    26. 26)
      • 19. Akram, U., Nadarajah, M., Raza, M.Q., et al: ‘Rocof restrictive planning framework and wind speed forecast informed operation strategy of energy storage system’, IEEE Trans. Power Syst., 2021, 36, (1), pp. 224234.
    27. 27)
      • 17. Liu, J., Tang, F., Zhao, J., et al: ‘Coherency identification for wind-integrated power system using virtual synchronous motion equation’, IEEE Trans. Power Syst., 2020, 35, (4), pp. 26192630.
    28. 28)
      • 8. Dreidy, M., Mokhlis, H., Mekhilef, S.: ‘Inertia response and frequency control techniques for renewable energy sources: a review’, Renew. Sustain. Energy Rev., 2017, 69, (July 2016), pp. 144155.
    29. 29)
      • 6. Adrees, A., Milanović, J.V., Mancarella, P.: ‘Effect of inertia heterogeneity on frequency dynamics of low-inertia power systems’, IET Gener. Transm. Distrib., 2019, 13, (14), pp. 29512958.
    30. 30)
      • 26. MIGRATE Work package 1: ‘MIGRATE deliverable D1.2: report on power system analysis and key performance indicators’, MIGRATE consortium, 2018. Available at www.h2020-migrate.eu.
    31. 31)
      • 28. Nikolopoulou, A.: ‘Wind turbine contribution to ancillary services under increased renewable penetration levels’. MSc thesis, Delft University of Technology, 2017.
    32. 32)
      • 25. Energynautics: ‘MIGRATE project, type-3 and type-4 EMT – model documentation’, Germany, 2017.
    33. 33)
      • 2. Rakhshani, E., Gusain, D., Sewdien, V., et al: ‘A key performance indicator to assess the frequency stability of converter dominated power system’, IEEE Access, 2019, 7, pp. 130957130969.
    34. 34)
      • 27. Pulgar-Painemal, H., Wang, Y., Silva-Saravia, H.: ‘On inertia distribution, inter-area oscillations and location of electronically-interfaced resources’, IEEE Trans. Power Syst., 2018, 33, (1), pp. 9951003.
    35. 35)
      • 38. Björnstedt, J.: ‘Integration of non-synchronous generation’. Doctroal Thesis, Lund University, 2012.
    36. 36)
      • 16. Zhong, Q., Member, S., Weiss, G.: ‘Synchronverters: inverters that mimic synchronous generators’, IEEE Trans. Ind. Electron., 2011, 58, (4), pp. 12591267.
    37. 37)
      • 31. Morren, J., Member, S., De Haan, S.W.H., et al: ‘Wind turbines emulating inertia and supporting primary frequency control’, IEEE Trans. Power Syst., 2006, 21, (1), pp. 20052006.
    38. 38)
      • 34. DIgSILENT: ‘DIgSILENT PowerFactory technical reference documentation phase measurement device ElmPhi pll’, Germany, 2016.
    39. 39)
      • 21. Fernández-Guillamón, A., Vigueras-Rodríguez, A., Molina-García, Á.: ‘Analysis of power system inertia estimation in high wind power plant integration scenarios’, IET Renew. Power Gener., 2019, 13, (15), pp. 28072816.
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