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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Implementation of a Simplified State Estimator for Wind Turbine Monitoring on an Embedded System

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DOI: http://dx.doi.org/10.15439/2017F250

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 11671175 ()

Full text

Abstract. The transition towards a cyber-physical energy system (CPES) entails an increased dependency on valid data. Simultaneously, an increasing implementation of renewable generation leads to possible control actions at individual distributed energy resources (DERs). A state estimation covering the whole system, including individual DER, is time consuming and numerically challenging. This paper presents the approach and results of implementing a simplified state estimator onto an embedded system for improving DER monitoring. The implemented state estimator is based on numerically robust orthogonal factorization and used on a set of state equations of a generic wind turbine generator (WTG). The simplified state estimator is tested by simulating a generic WTG model and evaluated based on its execution time and estimation accuracy. Results show its fast execution time, its accuracy in handling normal measurement error and its ability to provide reliable data in the case of gross errors in the set of measurements.

References

  1. P. Beiter and T. Tian, “2015 renewable energy data book,” U.S. Department of Energy’s National Renewable Energy Laboratory (NREL), Tech. Rep., 2016.
  2. Energinet.dk, “Technical regulation 3.2.5 for wind power plants above 11 kw,” Energinet.dk, Tech. Rep. 13/96336-43, 2016.
  3. Energinet.dk, “Technical regulation 3.2.2 for pv power plants above 11 kw,” Energinet.dk, Tech. Rep. 14/17997-39, 2016.
  4. F. F. Wu, K. Moslehi, and A. Bose, “Power system control centers: Past, present, and future,” Proceedings of the IEEE, vol. 93, no. 11, pp. 1890–1908, Nov 2005. http://dx.doi.org/10.1109/JPROC.2005.857499.
  5. X. Yu and Y. Xue, “Smart grids: A cyber-physical systems perspective,” Proc. IEEE, vol. 104, no. 5, pp. 1058–1070, May 2016. http://dx.doi.org/10.1109/JPROC.2015.2503119.
  6. R. Rajkumar, I. Lee, L. Sha, and J. Stankovic, “Cyber-physical systems: The next computing revolution,” in Design Automation Conference, June 2010, pp. 731–736. http://dx.doi.org/10.1145/1837274.1837461.
  7. S. Sridhar, A. Hahn, and M. Govindarasu, “Cyber-physical system security for the electric power grid,” Proceedings of the IEEE, vol. 100, no. 1, pp. 210–224, Jan 2012. http://dx.doi.org/10.1109/JPROC.2011.2165269.
  8. X. Shi, Y. Li, Y. Cao, and Y. Tan, “Cyber-physical electrical energy systems: challenges and issues,” CSEE Journal of Power and Energy Systems, vol. 1, no. 2, pp. 36–42, June 2015. http://dx.doi.org/10.17775/CSEEJPES.2015.00017.
  9. F. C. Schweppe, J. Wildes, and D. B. Rom, “Power system static-state estimation, parts I, II, III,” IEEE Transactions on Power Ap- paratus and Systems, vol. PAS-89, no. 1, pp. 120–135, Jan 1970. http://dx.doi.org/10.1109/TPAS.1970.292678.
  10. K. Clark, N. W. Miller, and J. J. Sanchez-Gasca, “Modeling of GE wind turbine-generators for grid studies,” GE Energy, Tech. Rep. Version 4.4, September 2009.
  11. S. Yu, K. Emami, T. Fernando, H. H. C. Iu, and K. P. Wong, “State estimation of doubly fed induction generator wind turbine in complex power systems,” IEEE Transactions on Power Systems, vol. 31, no. 6, pp. 4935–4944, Nov 2016. http://dx.doi.org/10.1109/TPWRS.2015.2507620.
  12. S. A. A. Shahriari, M. Raoofat, M. Dehghani, M. Mohammadi, and M. Saad, “Dynamic state estimation of a permanent magnet synchronous generator-based wind turbine,” IET Renewable Power Generation, vol. 10, no. 9, pp. 1278–1286, 2018. http://dx.doi.org/10.1049/iet-rpg.2015.0502.
  13. F. F. Wu, “Power system state estimation: a survey,” International Journal of Electrical Power & Energy Systems, vol. 12, no. 2, pp. 80–87, 1990. http://dx.doi.org/10.1016/0142-0615(90)90003-T.
  14. A. Monticelli, “Electric power system state estimation,” Proceedings of the IEEE, vol. 88, no. 2, pp. 262–282, Feb 2000. http://dx.doi.org/10.1109/5.824004.
  15. L. Holten, A. Gjelsvik, S. Aam, F. F. Wu, and W. H. E. Liu, “Comparison of different methods for state estimation,” IEEE Transactions on Power Systems, vol. 3, no. 4, pp. 1798–1806, Nov 1988. http://dx.doi.org/10.1109/59.192998.
  16. L. Mili, T. V. Cutsem, and M. R.-P. and, “Bad data identification methods in power system state estimation-a comparative study,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-104, no. 11, pp. 3037–3049, Nov 1985. http://dx.doi.org/10.1109/TPAS.1985.318945.
  17. H. J. Koglin, T. Neisius, G. Beißler, and K. D. Schmitt, “Bad data detection and identification,” International Journal of Electrical Power & Energy Systems, vol. 12, no. 2, pp. 94–103, 1990. http://dx.doi.org/10.1016/0142-0615(90)90005-V.
  18. E. Handschin, F. C. Schweppe, J. Kohlas, and A. Fiechter, “Bad data analysis for power system state estimation,” IEEE Transactions on Power Apparatus and Systems, vol. 94, no. 2, pp. 329–337, Mar 1975. http://dx.doi.org/10.1109/TPAS.1975.31858.
  19. A. Garcia, A. Monticelli, and P. Abreu, “Fast decoupled state estimation and bad data processing,” IEEE Transactions on Power Ap- paratus and Systems, vol. PAS-98, no. 5, pp. 1645–1652, Sept 1979. http://dx.doi.org/10.1109/TPAS.1979.319482.
  20. V. H. Quintana, A. Simoes-Costa, and M. Mier, “Bad data detection and identification techniques using estimation orthogonal methods,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-101, no. 9, pp. 3356–3364, Sept 1982. http://dx.doi.org/10.1109/TPAS.1982.317595.
  21. M. Schlechtingen, I. F. Santos, and S. Achiche, “Wind turbine condition monitoring based on SCADA data using normal behavior models. part 1: System description,” Applied Soft Computing, vol. 13, no. 1, pp. 259 – 270, 2013. http://dx.doi.org/10.1016/j.asoc.2012.08.033.
  22. J. R. Kristoffersen and P. Christiansen, “Horns Rev offshore windfarm: its main controller and remote control system,” Wind Engineering, vol. 27, no. 5, pp. 351 – 360, 2003. http://dx.doi.org/10.1260/030952403322770959.
  23. B. Badrzadeh, M. Bradt, N. Castillo, R. Janakiraman, R. Kennedy, S. Klein, T. Smith, and L. Vargas, “Wind power plant scada and controls,” in PES T D 2012, May 2012, pp. 1–7. http://dx.doi.org/10.1109/PES.2011.6039418.
  24. A. Ellis, Y. Kazachkov, J. Sanchez-Gasca, p. Pourbeik, E. Muljadi, M. Behnke, J. Fortmann, and S. Seman, Wind Power in Power Systems. John Wiley & Sons, Ltd., 2012, ch. 35: A Generic Wind Power Plant Model. ISBN: 9780470974162.
  25. B. P. Lathi, Signal Processing and Linear Systems, international ed. ed. Oxford, United Kingdom: Oxford University Press, 2010. ISBN: 978-0-19-539257-9.
  26. A. D. Hansen, P. Sørensen, F. Iov, and F. Blaabjerg, “Control of variable speed wind turbines with doubly-fed induction generators,” Wind Engineering, vol. 28, no. 4, pp. 411–432, 2004. http://dx.doi.org/10.1260/0309524042886441.
  27. K. E. Martin, “Synchrophasor measurements under the ieee standard c37.118.1-2011 with amendment c37.118.1a,” IEEE Transactions on Power Delivery, vol. 30, no. 3, pp. 1514–1522, June 2015. http://dx.doi.org/10.1109/TPWRD.2015.2403591.
  28. ABB, “XLPE submarine cable systems attachment to XLPE land cable systems - user’s guide,” Brochure, April 2010, rev. 5.
  29. J. D. Glover, M. S. Sarma, and T. J. Overbye, Power System Analysis and Design, 5th ed. Stamford, CT: Cengage Learning, 2008. ISBN: 978-1-111-42579-1.
  30. P. M. Anderson and A. Bose, “Stability simulation of wind turbine systems,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-102, no. 12, pp. 3791–3795, Dec 1983. http://dx.doi.org/10.1109/TPAS.1983.317873.
  31. X. R. Li and Z. Zhao, “Measures of performance for evaluation of estimators and filters,” vol. 4473, 2001, pp. 530–541. http://dx.doi.org/10.1117/12.492751.