access icon free Conventional and fuzzy PODCs for DFIG-based wind farms and their impact on inter-area and torsional oscillation damping

Contribution to the damping of inter-area and torsional oscillation modes, in doubly fed induction generators (DFIG) based wind farms, by power oscillation damping controllers (PODCs) based on two conventional structures and a fuzzy control strategy is investigated in this study. In this regard, a PODC with no lead/lag compensation is designed first and then a PODC with one lead/lag block is developed using eigenvalue techniques and the application of an iterative process based on the bat optimisation algorithm (BOA). Moreover, a fuzzy PODC, based on a simple fuzzy controller and tuned with the BOA according to the system transient response under a critical perturbation, is also designed. Comparative performance of the three PODCs is evaluated on a multi-machine power system. It is observed that all three PODCs can contribute to improving the damping of inter-area oscillations. However, eigenvalue analysis and non-linear time domain simulations reveal that each of them may also impact to a lesser or greater extent the shaft torsional oscillation mode damping. Their relative impact in this regard is also investigated.

Inspec keywords: iterative methods; fuzzy control; eigenvalues and eigenfunctions; machine control; wind power plants; oscillations; time-domain analysis; optimisation; damping; asynchronous generators

Other keywords: fuzzy PODC; BOA; torsional oscillation damping; eigenvalue technique; doubly fed induction generators based wind farms; nonlinear time domain simulation; multimachine power system; iterative process; fuzzy control; power oscillation damping controllers; interarea oscillation damping; eigenvalue analysis; bat optimisation algorithm; lead/lag compensation; DFIG based wind farms; shaft torsional oscillation mode damping

Subjects: Control of electric power systems; Interpolation and function approximation (numerical analysis); Mathematical analysis; Optimisation techniques; Fuzzy control; Asynchronous machines; Interpolation and function approximation (numerical analysis); Wind power plants; Mathematical analysis; Optimisation techniques

References

    1. 1)
      • 14. Zhao, P., Yao, W., Wang, S., et al: ‘Decentralized nonlinear synergetic power system stabilizers design for power system stability enhancement’, Int. Trans. Electr. Energy Syst., 2014, 24, pp. 3561368.
    2. 2)
      • 7. Rimorov, D., Kamwa, I., Joós, G.: ‘Coordinated design of active and reactive power modulation auxiliary loops of wind turbine generators for oscillation damping in power systems’. Proc. IEEE PES General Meeting, Denver, USA, July 2015, pp. 15.
    3. 3)
      • 19. Rogers, G.J.: ‘Control for stability in interconnected power systems’, IEEE Control Syst. Mag., 1989, 9, (1), pp. 1922.
    4. 4)
      • 31. Yang, X.-S.: ‘A new metaheuristic bat-inspired algorithm’, in Gonzalez, J.R., et al: ‘Studies in Computational Intelligence’ (Springer, 2010), pp. 6574.
    5. 5)
      • 33. SSAT-Small Signal Analysis Tool (V7.0), Powertech Labs Inc., Canada.
    6. 6)
      • 24. Haupt, R.L., Haupt, S.E.: ‘Practical genetic algorithms’ (John Wiley & Sons Inc., 2004).
    7. 7)
      • 36. Skogestad, S., Postlethwait, I.: ‘Multivariable feedback control: analysis and design’ (John Wiley & Sons, 2005).
    8. 8)
      • 34. Ramirez-Gonzalez, M., Malik, O.P.: ‘Simplified fuzzy logic controller and its application as a power system stabilizer’. Proc. of the IEEE 15th Int. Conf. on Intelligent System Applications to Power Systems, Curitiba, Brazil, November 2009, pp. 16.
    9. 9)
      • 28. El-Metwally, K.A., Malik, O.P.: ‘Application of fuzzy logic stabilizers in a multimachine power system environment’, IET Proc.– Gener. Transm. Distrib., 1996, 143, (3), pp. 15.
    10. 10)
      • 2. Ackermann, T.: ‘Wind power in power systems’ (John Wiley & Sons, 2005).
    11. 11)
      • 3. Fan, L., Yin, H., Miao, Z.: ‘On active/reactive power modulation of DFIG-based wind generation for interarea oscillation damping’, IEEE Trans. Energy Convers., 2011, 26, (2), pp. 513521.
    12. 12)
      • 30. TSAT-Transient Security Assessment Tool (V7.0), Powertech Labs Inc., Canada.
    13. 13)
      • 35. Ramirez-Gonzalez, M., Malik, O.P.: ‘Comparative performance of neuro-fuzzy PSS architectures with adaptive input link weights and nonlinear functions’. Proc. 17th IFAC World Congress, Seoul, Korea, July 2008, pp. 1393213937.
    14. 14)
      • 10. Miao, Z., Fan, L., Osborn, D., et al: ‘Control of DFIG-based wind generation to improve interarea oscillation damping’, IEEE Trans. Energy Convers., 2009, 24, (2), pp. 415422.
    15. 15)
      • 13. Pal, B.C.: ‘Robust pole placement versus root-locus approach in the context of damping interarea oscillations in power systems’, IET Proc.– Gener. Transm. Distrib., 2002, 149, (6), pp. 739745.
    16. 16)
      • 4. Domínguez-García, J.L., Bianchi, F.D., Gomis-Bellmunt, O.: ‘Analysis of the damping contribution of power system stabilizers driving wind power plants’, Wind Energy, 2014, 17, pp. 267278.
    17. 17)
      • 23. Yang, S.-X., Cui, Z., Xiao, R., et al: ‘Swarm intelligence and bio-inspired computation-theory and applications’ (Elsevier, 2013).
    18. 18)
      • 9. Huia, L., Shengquan, L., Haiting, J., et al: ‘Damping control strategies of inter-area low-frequency oscillation for DFIG-based wind farms integrated into a power system’, Electr. Power Energy Syst., 2014, 61, pp. 279287.
    19. 19)
      • 22. Engelbrecht, A.P.: ‘Computational intelligence: an introduction’ (John Wiley & Sons Ltd., 2007).
    20. 20)
      • 17. Ali, Z.M.M., Malikov, A.I.: ‘Robust techniques for designing power system stabilizers’, J. Theor. Appl. Inf. Technol., 2009, 9, (1), pp. 2028.
    21. 21)
      • 5. Surinkaew, T., Ngamroo, I.: ‘Power system oscillations damping by robust decentralized DFIG wind turbines’, J. Electr. Eng. Technol., 2015, 10, (1), pp. 3040.
    22. 22)
      • 6. Berrutti, F., Giusto, A., Artenstein, M.: ‘Design of power system stabilizers in variable speed wind generators using remote signals’. Proc. IEEE PES T&D LA, Montevideo, Uruguay, September 2012, pp. 18.
    23. 23)
      • 20. Agachi, P.S., Nagy, Z.K., Cristea, M.V., et al: ‘Model based control: case studies in process engineering’ (Wiley-VCH, 2006).
    24. 24)
      • 11. Gong, B., Xu, D., Wun, B.: ‘Network damping capability of DFIG-based wind farm’. Proc. IEEE Energy Conversion Congress and Exposition, Atlanta, USA, September 2010, pp. 40834090.
    25. 25)
      • 12. Taranto, G.N., Shiau, J.-K., Chow, J.H., et al: ‘Robust decentralised design for multiple FACTS damping controllers’, IET Proc.– Gener. Transm. Distrib., 1997, 144, (1), pp. 6167.
    26. 26)
      • 18. Choi, S.S., Lim, C.M.: ‘Design of wide range power system stabilizers via pole-placement technique’, Comput. Electr. Eng., 1982, 9, (2), pp. 103110.
    27. 27)
      • 29. ‘User-Defined Model Manual for TSAT and SSAT’ (Powertech, 2006).
    28. 28)
      • 32. Matlab®: The Language of Technical Computing (R2010b), The MathWorks Inc., USA.
    29. 29)
      • 25. Qing, A.: ‘Differential evolution: fundamentals and applications in electrical engineering’ (John Wiley & Sons, 2009).
    30. 30)
      • 1. Fox, B., Flynn, D., Bryans, L., et al: ‘Wind power integration: connection and system operational aspects’ (The Institution of Engineering and Technology, 2007).
    31. 31)
      • 8. Tsourakis, G., Nanou, S., Vournas, C.: ‘A power system stabilizer for variable-speed wind generators’. Proc. 18th IFAC World Congress, Milano, Italy, August 2011, pp. 1171311719.
    32. 32)
      • 26. Parpoulos, K.E., Vrahatis, M.N.: ‘Particle swarm optimization and intelligence: advances and applications’ (Information Science Reference, 2010).
    33. 33)
      • 16. Safari, A., Ahmadian, A., Holkar, M.A.A.: ‘Controller design of STATCOM for power system stability improvement using honey bee mating optimization’, J. Appl. Res. Technol., 2013, 11, pp. 144155.
    34. 34)
      • 15. Wang, H.F.: ‘Interaction and co-ordination of multiple-function FACTS controllers’, Eur. Trans. Electr. Power, 2001, 11, pp. 715.
    35. 35)
      • 21. Saxena, D., Singh, S.N., Verma, K.S.: ‘Application of computational intelligence in emerging power systems’, Int. J. Eng. Sci. Technol., 2010, 2, pp. 17.
    36. 36)
      • 27. Yang, X.-S.: ‘Bat algorithm: literature review and applications’, Int. J. Bio-Inspired Comput., 2013, 5, (3), pp. 141149.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2016.0138
Loading

Related content

content/journals/10.1049/iet-rpg.2016.0138
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading