Advertisement

Method, Algorithm and Module of a Parameter Estimation Program for Mathematical Models of Synchronous Generator and Excitation Systems

  • Stefan PaszekEmail author
  • Andrzej Boboń
  • Sebastian Berhausen
  • Łukasz Majka
  • Adrian Nocoń
  • Piotr Pruski
Chapter
  • 258 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 631)

Abstract

This chapter presents a description of the method for parameter estimation of mathematical models of generating unit elements. The parameters of mathematical models of individual elements of generating units can be determined on the basis of analysis of dynamic waveforms caused by appropriate disturbances (measurement tests) of the steady state of the system. In the approximation process, these parameters are selected in such a way as to minimize the mean square error, defined for deviations between the measured waveforms and the waveforms calculated on the basis of mathematical models. Calculation of the parameters of synchronous generator models (in the d and q axes) and excitation systems (with optionally separated submodels of these systems) was brought to minimization of properly defined objective functions. A detailed description and analysis of the use of selected algorithms to minimize these objective functions are presented. The most effective algorithm was selected. It is the hybrid algorithm. This chapter also presents a module of the PARGU program developed for the parameter estimation of mathematical models which allows performing step by step all activities necessary to calculate the parameters of mathematical models of elements of generating units based on the dynamic waveforms measured in a power plant.

Keywords

Parameter estimation algorithm Measurement tests Least squares method Objective function minimization Levenberg–Marquardt algorithm Genetic algorithm Hybrid algorithm 

References

  1. 1.
    Abd-Alla AN, Cheng SJ, Wen JY, Zhang J (2006) Model parameter identification of excitation system based on a genetic algorithm techniques. Paper presented at the international conference on power system technology, Oct 2006, pp 1–5Google Scholar
  2. 2.
    Bellavia S, Gratton S, Riccietti E (2018) A Levenberg-Marquardt method for large nonlinear least-squares problems with dynamic accuracy in functions and gradients. Numer Math 140(3):791–825Google Scholar
  3. 3.
    Berhausen S, Paszek S (2010) Use of pseudorandom signal for electromagnetic parameter estimation of synchronous generator with static exciter working in a power system. Paper presented at the 33th international conference on fundamentals of electrotechnics and circuit theory, SPETO, Ustroń, May 2010, pp 69–70Google Scholar
  4. 4.
    Berhausen S, Paszek S (2015) Assessment of the accuracy of synchronous generator model parameter estimation based on noisy dynamic waveforms. Przegląd Elektrotechniczny 7:16–20Google Scholar
  5. 5.
    Berhausen S, Paszek S (2016) Synchronous generator model parameter estimation based on noisy dynamic wave forms. J Electr Eng 67(1):21–28.  https://doi.org/10.1515/jee-2016-0003/CrossRefGoogle Scholar
  6. 6.
    Bhatti MA (2000) practical optimization methods. With mathematica applications. Springer, New YorkCrossRefGoogle Scholar
  7. 7.
    Białek J, Paszek S, Boboń A, Kudła J (2002) Synchronous machine parameter derivation program. Project ECP1219/C3762 sponsored by EPRI Solutions, Electric Power Research Institute, Inc. (Contractor: University of Durham, School of Engineering, Durham, UK), Paolo Alto, USA, May 2002Google Scholar
  8. 8.
    Boboń A, Kudła J, Ondrusek J (1998) Approximation of synchronous machine spectra transfer functions when using the genetic algorithm and Levenberg-Marquardt Method. Paper presented at the international workshop on electrical machines, Prague, Czech Republic, Sept 1998, pp 111–119Google Scholar
  9. 9.
    Boboń A, Paszek S, Pruski P, Kraszewski T, Bojarska M (2010) Matlab/Simulink program for determining parameter of generating unit models. Paper presented at the computational problems of electrical engineering, CPEE, Lazne Kynzvart, Czech Republic, Sept 2010, p 75Google Scholar
  10. 10.
    Boboń A, Paszek S, Pruski P, Kraszewski T, Bojarska M (2011) Computer-aided determining of parameters of generating unit models based on measurement tests. Przegląd Elektrotechniczny 87(5):17–21Google Scholar
  11. 11.
    Boboń A, Bojarska M, Kraszewski T, Majka Ł, Pasko M, Paszek S, Pruski P (2012) Computations of generating unit model parameters using program PARZW with database. Paper presented at the 10th international conference control of power systems, 15–17 May 2012 Tatranské Matliare, High Tatras, Slovak Republic, pp 169–170Google Scholar
  12. 12.
    Boboń A, Nocoń A, Paszek S, Pruski P (2017) Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm. Bull Pol Acad Sci 65(4):479–488.  https://doi.org/10.1515/bpasts-2017-0053CrossRefGoogle Scholar
  13. 13.
    Bortoni EC, Jardini JA (2002) Identification of synchronous machine parameters using load rejection test data. IEEE Trans Energy Convers 17(2):242–247CrossRefGoogle Scholar
  14. 14.
    Dehghani M, Karrari M, Rosehart W, Malik OP (2010) Synchronous machine model parameters estimation by a time-domain identification method. Electr Power Energy Syst 32:524–529CrossRefGoogle Scholar
  15. 15.
    Feltes JW, Lima LTG (2003) Validation of dynamic model parameters for stability analysis: industry need, current practices and future trends. Paper presented at the IEEE power engineering society general meeting, vol 3, 13–17 July 2003, pp 1295–1301Google Scholar
  16. 16.
    Feltes JW, Orero S, Fardanesh B, Uzunovic E, Zelingher S, Abi-Samra N (2002) Deriving model parameters from field test measurements. IEEE Comput Appl Power 30–36Google Scholar
  17. 17.
    Gallehdari Z, Dehghani M, Nikravesh SKY (2014) Online state space model parameter estimation in synchronous machines. Iran J Electr Electron Eng 10(2):124–132Google Scholar
  18. 18.
    Ghomi M, Najafi YS (2007) Review of synchronous generator parameters estimation and model identification. Paper presented at the 42nd international universities power engineering conference, UPEC, Sept 2007, pp 228–235Google Scholar
  19. 19.
    Glaninger-Katschning A (2009) Identification of excitation system transfer functions in hydro power plants using a binarypseudo random signal. Elektrotech Intech. Springer, Berlin, pp 8–12Google Scholar
  20. 20.
    Hasni M, Touhami O, Ibtiouen R, Fadel M, Caux S (2007) Synchronous machine parameter identification by various excitation signals. Electr Eng 219–228.  https://doi.org/10.10007/s00202-007-0069-z
  21. 21.
    Hemandez JA, Botero HA, Ospina JD, Perez JC (2006) Excitation system parameters estimation using evolutionary algorithms. Paper presented at the transmission and distribution conference and exposition, Latin America, TDC, 2006, pp 1–6Google Scholar
  22. 22.
    Heydt GT, Kyriakides E, Karady G, Holbert H, Borkar S, Gu W, Vittal V, Huang G, Men K (2005) Estimation of synchronous generator parameters from on-line measurements. Final Project Report, Power Systems Engineering Research Center Publication 05-36Google Scholar
  23. 23.
    Huang C, Chen Y, Chang C, Chiang H, Wang J (1994) On-line measurement-based parameter estimation for synchronous generators: model development and identification schemes. IEEE Trans Energy Convers 9:330–336CrossRefGoogle Scholar
  24. 24.
    Karayaka HB, Keyhani A, Agrawal B, Selin D, Heydt GT (1999) Methodology development for estimation of armature circuit and field winding parameters of large utility generators. IEEE Trans Energy Convers 14(4):901–908CrossRefGoogle Scholar
  25. 25.
    Karrari M, Malik OP (2004) Identification of physical parameters of a synchronous generator from online measurements. IEEE Trans Energy Convers 19(2):407–415CrossRefGoogle Scholar
  26. 26.
    Khereshki NI, Choolabi EF, Shahalami SH (2017) Identification of dynamic parameters of a fourth-order synchronous generator nonlinear model using particle swarm optimization algorithm. Paper presented at the Iranian conference on electrical engineering, 2–4 May 2017, Teheran, Iran, pp 1170–1174Google Scholar
  27. 27.
    Kraszewski T, Majka Ł, Pasko M, Paszek S (2010) Sensitivity analysis of the electromachine excitation system model. Paper presented at the 9th international conference control of power systems 2010, Tatranske Matliare, Slovak Republic, May 2010, pp 29–30Google Scholar
  28. 28.
    Layer E (2002) Modelling of simplified dynamical systems. Springer, BerlinCrossRefGoogle Scholar
  29. 29.
    Li MY (2018) An introduction to mathematical modeling of infectious diseases. Springer, ChamCrossRefGoogle Scholar
  30. 30.
    Majka Ł, Paszek S (2010) Sensitivity analysis of the steam turbine and its governor mathematical model. Paper presented at the 33th international conference on fundamentals of electrotechnics and circuit theory, SPETO, Ustroń, May 2010, pp 67–68Google Scholar
  31. 31.
    Majka Ł, Paszek S (2010) Algorithms for estimation of models parameters of excitation system of an electrical machine. Acta Tech CSAV, Acad Sci Czech Repub 55(2):179–194Google Scholar
  32. 32.
    Majka Ł, Paszek S (2016) Matematical model parameter estimation of a generating unit operating in the polish national power system. Bull Pol Acad Sci 64(2):409–416Google Scholar
  33. 33.
    Man KF, Tang KS, Kwong S (1999) Genetic algorithms. Concepts and designs. Springer, LondonCrossRefGoogle Scholar
  34. 34.
    Mathworks, Inc.: Optimization toolbox user’s guide (1990–2001)Google Scholar
  35. 35.
    Mouni E, Tnani S, Champenois G (2008) Synchronous generator modelling and parameters estimation using least squares method. Simul Model Pract Theory 16:678–689CrossRefGoogle Scholar
  36. 36.
    Nocoń A, Pasko M, Paszek S (2010) Sensitivity analysis including uncertainty of synchronous generator model parameter. Paper presented at the 9th international conference control of power systems 2010, Tatranske Matliare, Slovak Republic, May 2010, pp 27–28Google Scholar
  37. 37.
    Nocoń A, Boboń A, Paszek S, Pasko M, Pruski P, Majka Ł, Szuster D, Bojarska M (2011) Measurement parameter estimation of the model of a synchronous generator working in thermal electric power plant. Paper presented at the 10th international conference on advanced methods in the theory of electrical engineering, AMTEE, 6–9 Sept 2011, Klatovy, Czech Republic, pp VI-3-4Google Scholar
  38. 38.
    Ortiz-Boyer D, Harvás-Martínez C, Muñoz-Pérez J (2003) Study of genetic algorithms with crossover based on confidence intervals as an alternative to classical least squares estimation methods for nonlinear models. Metaheuristics: computer decision-making. Appl Optim 86:127–151Google Scholar
  39. 39.
    Paszek S, Majka Ł (2008) Computations of the model parameters of generating unit elements based on measurements. AT&P J Plus 2, Power Syst Stab 49–53Google Scholar
  40. 40.
    Paszek S, Boboń A, Kudła J, Białek J, Abi-Samra N (2005) Parameter estimation of the mathematical model of a generator, excitation system and turbine. Przegląd Elektrotechniczny 81(11):7–12Google Scholar
  41. 41.
    Santana MM, Ferreira R, Costa F, Lima C (2015) A novel prony approach for synchronous generator parameter estimation. Przegląd Elektrotechniczny 81(1):50–54.  https://doi.org/10.15199/48.2015.01.09CrossRefGoogle Scholar
  42. 42.
    Schmitt LM (2001) Theory of genetic algorithms. Theoret Comput Sci 259:1–61MathSciNetCrossRefGoogle Scholar
  43. 43.
    Sebaa K, Boudour M (2007) Combination and genetic algorithms for the locations and tuning of robust power system stabilizers. Arch Control Sci 17(2):145–162zbMATHGoogle Scholar
  44. 44.
    Simond JJ, TuXuan M, Barz V (2002) Synchronous machine’s parameter determination using two phase short circuit test. Paper presented at the workshop on variable reluctance electrical machines, Technical University of Cluj-Napoca, 17 Sept 2002Google Scholar
  45. 45.
    Vermeulen HJ, Strauss JM, Shikoana V (2002) Online estimation of synchronous generator parameters using PRBS perturbations. IEEE Trans Power Syst 17(3):674–700Google Scholar
  46. 46.
    Wamkeue R, Baetscher F, Kamwa I (2008) Hybrid-state-model-based time-domain identification of synchronous machine parameters from saturated load rejection test records. IEEE Trans Energy Convers 23(1):68–77CrossRefGoogle Scholar
  47. 47.
    Zaker B, Gharenpetian GB, Karrari M, Moaddabi N (2015) Simultaneous parameter identification of synchronous generator and excitation system using online measurements. IEEE Trans Smart Grid PP(99):1–9Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Stefan Paszek
    • 1
    Email author
  • Andrzej Boboń
    • 2
  • Sebastian Berhausen
    • 3
  • Łukasz Majka
    • 4
  • Adrian Nocoń
    • 5
  • Piotr Pruski
    • 6
  1. 1.Faculty of Electrical EngineeringSilesian University of TechnologyGliwicePoland
  2. 2.Faculty of Electrical EngineeringSilesian University of TechnologyGliwicePoland
  3. 3.Faculty of Electrical EngineeringSilesian University of TechnologyGliwicePoland
  4. 4.Faculty of Electrical EngineeringSilesian University of TechnologyGliwicePoland
  5. 5.Faculty of Electrical EngineeringSilesian University of TechnologyGliwicePoland
  6. 6.Faculty of Electrical EngineeringSilesian University of TechnologyGliwicePoland

Personalised recommendations