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WAMS based identification for obtaining linear models to coordinate controllable devices

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Abstract

This paper is concerned with the use of subspace system identification techniques to derive a low-order black-box state-space model of a power system with many controllable devices. This is a multi-input multi-output open system model describing the power oscillatory behavior of the power system. The input signals are the controllable set points of the controllable devices, the output signals are the speed of some generators measured by a wide-area measurement system. This paper describes how to achieve and pre-process the data to use the subspace method to estimate and validate to finally assign an accurate model. This new approach can be used directly for the design of a centrally coordinated controller coordinating all the relevant controllable devices, with the aim to increase the damping of the modes in the system. Previously presented models, using input signals from controllable devices, use local measurements or output signals dependent on the actual operational point. The benefit of the presented method is that the used output signals are independent of the system state. This makes it possible to use a state-feedback controller, i.e., coordinated control. The presented method is applied in the Cigré Nordic 32-bus system including two high-voltage direct current (HVDC) links. The case study demonstrates that accurate low-order state-space models can be estimated and validated using the described method to accurately model the system’s power oscillatory behavior.

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References

  1. ABB (2009) Fenno-skan. http://www.abb.com/cawp/gad02181/c1256d71001e0037c1256b870030222b.aspx

  2. Adapa R (2002) FACTS system studies. IEEE Power Eng Rev 22: 17–22

    Article  Google Scholar 

  3. Dehghani M, Nikravesh SKY (2008) State-space model parameter identification in large-scale power systems. IEEE Trans Power Syst 23: 1449–1457

    Article  Google Scholar 

  4. Eriksson R, Knazkins V (2008) On the coordinated control of multiple HVDC links: modal analysis approach. GMSARN Int J 2: 15–20

    Google Scholar 

  5. Fatehi F, Smith JR, Pierre DA (1996) Robust power system controller design based on measured models. IEEE Trans Power Syst 11: 774–780

    Article  Google Scholar 

  6. Favoreel W, Moor BD, Overschee VP (2005) Subspace state space system identification for industrial processes. J Process Control 29: 687–688

    Google Scholar 

  7. Ghhasemi H, Canizares C, Moshref A (2006) Oscillatory stability limit prediction using stochastic subspace identification. IEEE Trans Power Syst 21: 736–745

    Article  Google Scholar 

  8. Handschin E (1972) Real-time control of electric power systems. Elsevier, Verlag-Amsterdam

    Google Scholar 

  9. Hingorani NG, Gyugyi L (2000) Understanding FACTS. IEEE Pres, Piscataway

    Google Scholar 

  10. Ho BL, Kalman RE (1966) Efficient construction of linear state variable models from input/output functions. Regelungstechnik 14: 545–548

    MATH  Google Scholar 

  11. Kailath T (1980) Linear Systems., Prentice-Hall

  12. Kamwa I (2000) Using mimo system identification for modal analysis and globalstabilization of large power systems. In: Proceedings on IEEE Power Engineering Society Summer Meeting

  13. Kamwa I, Grin-Lajoie L (2000) State-space system identification —toward MIMO models for modal analysis and optimazation of bulk power systems. IEEE Trans Power Syst 15: 326–334

    Article  Google Scholar 

  14. Kamwa I, Grondin R, Dickinson E, Fortin S (1993) A minimal realization approach to reduced-order modelling and modal analysis for power system response signals. IEEE Trans Power Syst 8: 1020–1029

    Article  Google Scholar 

  15. Kamwa I, Trudel G, Gerin-Lajoie L (1996) Low-order black-box models for control system design in large power systems. IEEE Trans Power Syst 11: 303–311

    Article  Google Scholar 

  16. Kundur P (1993) Power System Stability and Control. McGraw-Hill Inc, San Francisco

    Google Scholar 

  17. Larimore W (1990) Canonical variate analysis in identification, filtering, and adaptive control. In: Proceedings of the 29th IEEE Conference on Decision and Control

  18. Levine WS (1996) The control handbook. CRC Press, Boca Raton

    MATH  Google Scholar 

  19. Ljung L (1999) System identification: theory for the user. Prentice Hall Information and System Sciences Series, Upper Saddle River

    Google Scholar 

  20. Lu C, Li L, He J, Wu X, Li P (2007) Optimal coordinate design of multiple HVDC modulation controllers based on MIMO system identification. In: Proceedings on IEEE Power Engineering Society General Meeting

  21. Matlab (2009) System identification toolbox 7.3.1. http://www.mathworks.com/products/sysid/

  22. Mittelstadt WA, Krause PE, Overholt PN, Hauer JF, E.Wilson R, Rizy DT (1995) The DOE wide area measurement system (WAMS) project: Demonstration of dynamic information technology for future power system. In: Fault and Disturbance Analysis & Precise Measurements in Power Systems, Session 4, Arlington, VA

  23. Morison K, Wang L, Kundur P (2004) Power system security assessment. Power Energy Mag 4: 30–39

    Article  Google Scholar 

  24. Nordel (2009) Nordic grid code 2007. http://www.nordel.org

  25. Okamoto H, Kurita A, Sanches-Gasca JJ, Clark K, Miller NW, Chowhury JH (1996) Identification of equivalent linear power system models from electromagnetic transient time domain simulations using prony’s method. In: Proceedings of the 35th Conference on Decision and Control, Kobe, Japan

  26. Oppenheim AV, Willsky AS, Hamid WS (1996) Siganls & Systems. Prentice Hall, Upper Saddle River

    Google Scholar 

  27. Overschee P, Moor B (1994) N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica 30: 75–93

    Article  MATH  Google Scholar 

  28. Overschee PV, Moor BD (1996) Subspace Identification for Linear Systems. Kluwer Academic, MA

    Book  MATH  Google Scholar 

  29. Palmer E, Ledwich G (1996) Optimal placement of angle transducers in power systems. Trans Power Syst 11: 788–793

    Article  Google Scholar 

  30. Pierre DTJ, Zhou N, Hauer J, Parashar M (2008) Performance of three mode-meter block-processing algorithms for automated dynamic stability assessment. IEEE Trans Power Syst 23: 680–690

    Article  Google Scholar 

  31. Sanchez-Gasca JJ, Clark K, Miller NW, Okamoto H, Kurita A, Chow JH (1997) Identifying linear models from time domain simulations. IEEE Comput Appl Power 10: 26–30

    Article  Google Scholar 

  32. Smed T, Andersson G (1993) Utilising HVDC to damp power oscillations. IEEE Trans Power Deliv 8: 620–627

    Article  Google Scholar 

  33. Smith MA, Phadke AG (1995) Synchronized phasor measurement feedback for control of FACTS devices. In: Fault and Disturbance Analysis and Precise Measurements in Power Systems Conference, Arlington, VA

  34. Stubbe CM (1995) Long term dynamics phase II, tf 38-02-08. Tech rep, Cigré

  35. Tomiyama K, sato M, Yamaji K, Sekita M, Goto M (1998) Power swing damping control by HVDC power modulation in an AC/DC hybrid transmission system. Electr Eng 124: 938–944

    Google Scholar 

  36. Uhlen K, Warland L, Gjerde JO, Breidablik O, Uusitalo M, Leirbukt AB, Korba P (2008) Monitoring amplitude, frequency and damping of power system oscillations with pmu measurements. In: IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century

  37. U.S.–Canada power system outage task force (2004) final report on the August 14, 2003 blackout in the United States and Canada: Causes and recommendations. Tech rep, NERC

  38. Van Der Veen AJ, Deprettere EF, Swindlehurst AL (1993) Subspace-based signal analysis using singular value decompsition. Proc IEEE 81: 1277–1308

    Article  Google Scholar 

  39. van Overschee P (1995) Subspace identification: theory—implementation—application. Ph.D. thesis, Katholieke Universiteit Leuven

  40. Witzmann R (2001) Damping of interarea oscillations in large interconnected power systems. In: Proceedings on the International Conference on Power Systems Transients

  41. Working Group 14.29 (1999) 149 Coordination of controls of multiple FACTS/HVDC links in the same system. Tech rep, Cigré

  42. Xiao J, Xie X, Han Y, Wu J (2004) Dynamic tracking of low-frequency oscillations with improved prony method in wide-area measurement system. In: Power Engineering Society General Meeting, 2004. IEEE

  43. Zhou N, Pierre J, Hauer J (2006) Initial results in power system identification from injected probing signals using a subspace method. IEEE Trans Power Syst 21: 1296–1302

    Article  Google Scholar 

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Authors and Affiliations

  1. Electric Power Systems, Royal Institute of Technology, Stockholm, Sweden

    Robert Eriksson & Lennart Söder

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  1. Robert Eriksson
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  2. Lennart Söder
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Correspondence to Robert Eriksson.

Additional information

The financial support provided by the Centre of Excellence in Electrical Engineering EKC2 at the Royal Institute of Technology is gratefully acknowledged.

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Eriksson, R., Söder, L. WAMS based identification for obtaining linear models to coordinate controllable devices. Electr Eng 94, 27–36 (2012). https://doi.org/10.1007/s00202-011-0201-y

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