Estimation of Equivalent Model for Cluster of Induction Generator Based on PMU Measurements

  • Francisco M. Gonzalez-Longatt
  • José Luis Rueda
  • C. A. Charalambous
  • P. De Oliveira
Chapter
Part of the Power Systems book series (POWSYS)

Abstract

The induction generator (IG) is widely used in many applications due to its simplicity and ease of operation. Typical applications involve the following: split-shaft micro-turbines (SSMT), mini-hydro (MH), and fixed-speed wind turbines (FSWT). The accurate knowledge of the machine parameters is especially important in order to establish the performance of the IG as well as to directly affect its operational and control characteristics. The problem of IG parameter estimation results specially complicated to solve when a cluster of IG is interconnected to create of virtual power plant (VPP). Then, it is desirable to have an effective method to estimate the parameters of an equivalent model for a cluster of IG (which does not require detailed definition of the power plant structure and parameters) by using novel digital measurement equipment such as phasor measurement units (PMU) in transmission and distribution networks. This chapter presents a method for the estimation of an equivalent model (named as EqMCIG App) for a cluster of IG, based on the response to a system frequency disturbance. The performance and robustness of the method are evaluated using two different test systems where the EqMCIG is identified using the variable metric method (VMM). Numerical results demonstrate the viewpoint and effectiveness of the proposed methodology. This chapter have three main contributions: (i) Performing “parameter estimation” using ComIdent command (ii) The use of DSL model on model parameter identification (composite Frame, block definition—BlkDef) (iii) Use of a “Measurement File” object (ElmFile) as inputs variables.

Keywords

Fixed-speed wind turbines Generator modeling Induction machine Parameter estimation 

Supplementary material

321979_1_En_20_MOESM1_ESM.zip (294 kb)
Supplementary material 1 (ZIP 294 kb)

References

  1. 1.
    Gonzalez-Longatt F (2014) Frequency control and inertial response schemes for the future power networks. In: Hossain J, Mahmud A (eds) Large scale renewable power generation. Springer, Singapore, pp 193–231CrossRefGoogle Scholar
  2. 2.
    Chauhan RK, Rajpurohit BS, Singh SN, Gonzalez-Longatt FM (2014) DC grid interconnection for conversion losses and cost optimization. In: Hossain J, Mahmud A (eds) Renewable energy integration. Springer, Singapore, pp 327–345CrossRefGoogle Scholar
  3. 3.
    Gonzalez-Longatt F, Regulski P, Wall P, Terzija V (2011) Induction generator model parameter estimation using improved particle swarm optimization and on-line response to a change in frequency. Presented at the IEEE on the power and energy society general meeting, Detroit, 2011Google Scholar
  4. 4.
    de Mello FP, Feltes JW, Hannett LN, White JC (1982) Application of induction generators in power systems. IEEE Trans Power Apparatus Syst PAS-101:3385–3393CrossRefGoogle Scholar
  5. 5.
    Simões MG, Farret FA (2006) Renewable energy systems: design and analysis with induction generators. CRC Press, Boca RatonGoogle Scholar
  6. 6.
    Simões MG, Farret FA (2007) Alternative energy systems: design and analysis with induction generators (Power electronics and applications series). CRC Press, Boca RatonGoogle Scholar
  7. 7.
    Lai LL, Chan TF (2007) Distributed generation—induction and permanent magnet generators. Wiley, New JerseyGoogle Scholar
  8. 8.
    Al-Hinai A, Schoder K, Feliachi A (2003) Control of grid-connected split-shaft microturbine distributed generator. In: Proceedings of the 35th southeastern symposium on system theory, pp 84–88Google Scholar
  9. 9.
    Murthy SS, Jha CS, Ghorashi AH, Nagendra Rao PS (1989) Performance analysis of grid connected induction generators driven by hydro/wind turbines including grid abnormalities. In: Proceedings of the 24th intersociety conference energy conversion engineering (IECEC-89), vol 4. pp 2045–2050Google Scholar
  10. 10.
    Abdin ES, Xu W (1998) Control design and dynamic performance analysis of a wind turbine-induction generator unit. In: Proceedings of international conference on power system technology (POWERCON ‘98), vol 2. pp 1198–1202Google Scholar
  11. 11.
    Ansuj S, Shokooh F, Schinzinger R (1988) Parameter estimation for induction machines based on sensitivity analysis. In: Industrial applications society 35th annual petroleum and chemical industry conference, 1988, record of conference papers, pp 35–40Google Scholar
  12. 12.
    Velez-Reyes M, Minami K, Verghese GC (1989) Recursive speed and parameter estimation for induction machines. In: Conference record of the 1989 IEEE industry applications society annual meeting, 1989, vol 1. pp 607–611Google Scholar
  13. 13.
    Bishop RR, Richards GG (1990) Identifying induction machine parameters using a genetic optimization algorithm. In: Proceedings of Southeastcon ‘90, IEEE, 1990, vol 2. pp. 476–479Google Scholar
  14. 14.
    Huang KS, Wu QH, Turner DR (2002) Effective identification of induction motor parameters based on fewer measurements. IEEE Trans Energy Convers 17:55–60CrossRefGoogle Scholar
  15. 15.
    Sag T Cunkas M (2007) Multiobjective genetic estimation to induction motor parameters. In: International aegean conference on electrical machines and power electronics (ACEMP ‘07), pp 628–631Google Scholar
  16. 16.
    Huynh DC, Dunnigan MW (2010) Parameter estimation of an induction machine using advanced particle swarm optimisation algorithms. Electr Power Appl IET 4:748–760CrossRefGoogle Scholar
  17. 17.
    Huynh DC, Dunnigan MW (2010) Parameter estimation of an induction machine using a dynamic particle swarm optimization algorithm. In: IEEE international symposium on industrial electronics (ISIE), 2010, pp 1414–1419Google Scholar
  18. 18.
    Marino P, Mungiguerra V, Russo F, Vasca F, (1996) Parameter and state estimation for induction motors via interlaced least squares algorithm and Kalman filter. In: IEEE 27th annual conference on power electronics specialists, PESC ‘96, 1996, vol 2. pp 1235–1241Google Scholar
  19. 19.
    Ursem RK, Vadstrup P (2003) Parameter identification of induction motors using differential evolution. In: The 2003 Congress on Evolutionary Computation, CEC ‘03, 2003, vol 2. pp 790–796Google Scholar
  20. 20.
    Karimi A, Choudhry MA, Feliachi A (2007) PSO-based evolutionary optimization for parameter identification of an induction motor. In: Proceedings of 39th North American power symposium, 2007, NAPS ‘07, pp 659–664Google Scholar
  21. 21.
    Chen G, Guo W, Huang K (2007) On line parameter identification of an induction motor using improved particle swarm optimization. In: Chinese control conference, 2007, CCC 2007, pp 745–749Google Scholar
  22. 22.
    Bakari KE, Kling WL (2010) Virtual power plants: an answer to increasing distributed generation. In: Conference on innovative smart grid technologies (ISGT Europe), 2010 IEEE PES, pp 1–6Google Scholar
  23. 23.
    González-Longatt F, Regulski P, Wall P, Terzija V (2011) Fixed speed wind generator model parameter estimation using improved particle swarm optimization and system frequency disturbances. In: The 1st IET conference on renewable power generation, RPG 2011, Edinburgh, pp 1–5Google Scholar
  24. 24.
    Terzija V (2007) Wide area monitoring protection and control—WAMPAC. In: International conference on information and communication technology in electrical sciences (ICTES 2007), pp I-1Google Scholar
  25. 25.
    Terzija V, Valverde G, Deyu C, Regulski P, Madani V, Fitch J et al (2011) Wide-area monitoring, protection, and control of future electric power networks. In: Proceedings of the IEEE, vol 99. pp 80–93 Google Scholar
  26. 26.
    Kampisios K, Zanchetta P, Gerada C, Trentin A (2008) Identification of induction machine electrical parameters using genetic algorithms optimization. In: Industry applications society annual meeting, IAS ‘08, IEEE, 2008, pp 1–7Google Scholar
  27. 27.
    Filho EBS, Lima AMN, Jacobina CB (1991) Parameter estimation for induction machines via non-linear least squares method. In: Proceedings of international conference on industrial electronics, control and instrumentation, IECON ’91, 1991, vol 1. pp 639–643Google Scholar
  28. 28.
    de Oliveira PJR, Seixas PF, Aguirre LA, Peixoto ZMA (1998) Parameter estimation of a induction machine using a continuous time model. In: Proceedings of the 24th annual conference of the IEEE on industrial electronics society, IECON ‘98, 1998, vol 1. pp 292–296Google Scholar
  29. 29.
    Fletcher R (1970) A new approach to variable metric algorithms. Comput J 13:317–322CrossRefMATHGoogle Scholar
  30. 30.
    Fletcher R, Powell MJD (1963) A rapidly convergent descent method for minimization. Comput J 6:163–168CrossRefMATHMathSciNetGoogle Scholar
  31. 31.
    Fletcher R, Powell MJD (1987) Practical methods of optimization, 2nd edn. Wiley, New YorkMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Francisco M. Gonzalez-Longatt
    • 1
  • José Luis Rueda
    • 2
  • C. A. Charalambous
    • 3
  • P. De Oliveira
    • 4
  1. 1.School of Electronic, Electrical and Systems EngineeringLoughborough UniversityLoughboroughUK
  2. 2.Department of Electrical Sustainable EnergyDelft University of TechnologyDelftThe Netherlands
  3. 3.Department of Electrical and Computer EngineeringUniversity of CyprusNicosiaCyprus
  4. 4.The Energy Institute of Simon Bolivar UniversityCaracasVenezuela

Personalised recommendations