Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

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

verfasst von : Stefan Paszek, Andrzej Boboń, Sebastian Berhausen, Łukasz Majka, Adrian Nocoń, Piotr Pruski

Erschienen in: Synchronous Generators and Excitation Systems Operating in a Power System

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Analysis of the Sensitivity of Generating Unit Simulation Waveforms to Changes of Model Parameters
Nächstes Kapitel Forming and Filtration of Measurement Waveforms
Literatur
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 1–5
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):791–825
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 69–70
4.
Berhausen S, Paszek S (2015) Assessment of the accuracy of synchronous generator model parameter estimation based on noisy dynamic waveforms. Przegląd Elektrotechniczny 7:16–20
5.
6.
Bhatti MA (2000) practical optimization methods. With mathematica applications. Springer, New YorkCrossRef
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 2002
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 111–119
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 75
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):17–21
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 169–170
12.
13.
Bortoni EC, Jardini JA (2002) Identification of synchronous machine parameters using load rejection test data. IEEE Trans Energy Convers 17(2):242–247CrossRef
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 32:524–529CrossRef
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 1295–1301
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 30–36
17.
Gallehdari Z, Dehghani M, Nikravesh SKY (2014) Online state space model parameter estimation in synchronous machines. Iran J Electr Electron Eng 10(2):124–132
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 228–235
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 8–12
20.
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 1–6
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 05-36
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 9:330–336CrossRef
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):901–908CrossRef
25.
Karrari M, Malik OP (2004) Identification of physical parameters of a synchronous generator from online measurements. IEEE Trans Energy Convers 19(2):407–415CrossRef
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 1170–1174
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 29–30
28.
Layer E (2002) Modelling of simplified dynamical systems. Springer, BerlinCrossRef
29.
Li MY (2018) An introduction to mathematical modeling of infectious diseases. Springer, ChamCrossRef
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 67–68
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):179–194
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):409–416
33.
Man KF, Tang KS, Kwong S (1999) Genetic algorithms. Concepts and designs. Springer, LondonCrossRef
34.
Mathworks, Inc.: Optimization toolbox user’s guide (1990–2001)
35.
Mouni E, Tnani S, Champenois G (2008) Synchronous generator modelling and parameters estimation using least squares method. Simul Model Pract Theory 16:678–689CrossRef
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 27–28
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-3-4
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 86:127–151
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 49–53
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):7–12
41.
42.
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):145–162MATH
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 2002
45.
Vermeulen HJ, Strauss JM, Shikoana V (2002) Online estimation of synchronous generator parameters using PRBS perturbations. IEEE Trans Power Syst 17(3):674–700
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):68–77CrossRef
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):1–9
Metadaten
Titel
Method, Algorithm and Module of a Parameter Estimation Program for Mathematical Models of Synchronous Generator and Excitation Systems
verfasst von
Stefan Paszek
Andrzej Boboń
Sebastian Berhausen
Łukasz Majka
Adrian Nocoń
Piotr Pruski
Copyright-Jahr
2020
Buch
Synchronous Generators and Excitation Systems Operating in a Power System

Print ISBN: 978-3-030-37975-9

Electronic ISBN: 978-3-030-37976-6

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-37976-6_4