Advertisement

Phasor Measurement-Enabled Decision Making

Chapter
  • 987 Downloads
Part of the Power Electronics and Power Systems book series (PEPS)

Abstract

The controls developed in the previous chapter were continuous feedback controls, i.e., the control, u(t), depends on the state, x(t), at each instant of time. If x(t) changes, then u(t) changes with a small delay induced by communication latency (actually u(t) = f(x(t − Δt)). The power system, however, has other types of control which can be characterized as discrete in their dependence on state.

Keywords

Plausibility Check Augmented State Vector Complex Voltage Linear State Estimator Continuous Feedback Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Rovnyak, S., Taylor, C. W., Mechenbier, J. R., & Thorp, J. S. (1995). Plans to demonstrate decision tree control using phasor measurements for HVDC fast power changes. In Conference on Fault and Disturbance Analysis and Precise Measurements in Power Systems, Arlington, VA, November 9, 1995.Google Scholar
  2. 2.
  3. 3.
    Rovnyak, S., Taylor, C. W., & Thorp, J. S. (1995). Real-time transient stability prediction—Possibilities for on-line automatic database generation and classifier training. In Second IFAC Symposium on Control of Power Plants and Power Systems, Cancun, Mexico, December 7, 1995.Google Scholar
  4. 4.
    Breiman, L., Friedman, J. H., Olshen, R., & Stone, C. J. (1984). Classification and regression tree. Pacific California: Wadsworth & Brooks/Cole Advanced Books & Software.zbMATHGoogle Scholar
  5. 5.
    Swarnkar, A., & Niazi, K. R. (2005). CART for online security evaluation and preventive control of power systems. In Proceedings of the 5th WSEAS/IASME International Conference on Electric Power Systems, High Voltages, Electric Machines, Tenerife, Spain, 16–18 December 2005, pp. 378–383.Google Scholar
  6. 6.
    Bernabeu, E. E., Thorp, J. S., & Centeno, V. A. (2012). Methodology for a security/dependability adaptive protection scheme based on data mining. IEEE Transactions on Power Delivery, 27(1), 104–111.CrossRefGoogle Scholar
  7. 7.
    Murthy, S. K., Kasif, S., & Salzberg, S. (1994). A system for induction of oblique decision trees. J. Artificial Intelligence Research, 2(1), 1–32.zbMATHGoogle Scholar
  8. 8.
    Garlapati, S., & Thorp, J. S. (2011). Choice of reference in CART applications using PMU data. In Proceedings of the 17th Power Systems Computation Conference (PSCC), Stockholm, Sweden, 22–26 August 2011, pp. 1–7.Google Scholar
  9. 9.
    Fisher, R. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.CrossRefGoogle Scholar
  10. 10.
    Li, M., Pal, A., Phadke, A. G., & Thorp, J. S. (2013). Transient stability prediction based on apparent impedance trajectory recorded by PMUs. International Journal of Electrical Power and Energy Systems, 54, 498–504.CrossRefGoogle Scholar
  11. 11.
    Gao, F., Thorp, J. S., Gao, S., Pal, A., & Vance, K. A. (2015). A voltage phasor based fault classification method for PMU only state estimator output. Electric Power Components and Systems, 43(1), 22–31.CrossRefGoogle Scholar
  12. 12.
    Wang, T., Pal, A., Thorp, J. S., Wang, Z., Liu, J., & Yang, Y. (2015). Multi-polytope based adaptive robust damping control in power systems using CART. IEEE Transactions on Power Systems, 30(4), 2063–2072.CrossRefGoogle Scholar
  13. 13.
    IEEE Standard Definitions for Power Switchgear. In IEEE Std. C37.100-1992, 1992, pp. 1–82.Google Scholar
  14. 14.
    Horowitz, S. H., & Phadke, A. G. (1995). Power system relaying (2nd ed.). New York: Wiley.Google Scholar
  15. 15.
    Bernabeu, E. E. (2009). Methodology for a security-dependability adaptive protection scheme based on data mining. Ph. D. Dissertation, Virginia Tech.Google Scholar
  16. 16.
    Pal, A., Thorp, J. S., Khan, T., & Young, S. S. (2013). Classification trees for complex synchrophasor data. Electric Power Components and Systems, 41(14), 1381–1396.CrossRefGoogle Scholar
  17. 17.
    Jones, K. D., Thorp, J. S., & Gardner, R. M. (2013). Three-phase linear state estimation using phasor measurements. In Proceedings of the IEEE Power Engineering Society General Meeting, Vancouver, BC, Canada, 21–25 July 2013, pp. 1–5.Google Scholar
  18. 18.
    Pal, A. (2014). PMU-based applications for improved monitoring and protection of power systems. Ph. D. Dissertation, Virginia Tech.Google Scholar
  19. 19.
    Ghiocel, S. G., Chow, J. H., Stefopoulos, G., Fardanesh, B., Maragal, D., & Blanchard, B. (2014). Phasor-measurement-based state estimation for synchrophasor data quality improvement and power transfer interface monitoring. IEEE Trans on Power Systems, 29(2), 881–888.CrossRefGoogle Scholar
  20. 20.
    Gao, F., Thorp, J. S., Pal, A., & Gao, S. (2012). Dynamic state prediction based on Auto-Regressive (AR) model using PMU data. In Proceedings of the IEEE Power and Energy Conference at Illinois (PECI), Champaign, IL, 24–25 February 2012, pp. 1–5.Google Scholar
  21. 21.
    Eisinbergt, A., & Pugliese, P. (1994). Exact inversion of a class of Vandermonde matrices. In Proceedings of the Fifth SIAM Conference on Applied Linear Algebra, June 1994, pp. 239–243.Google Scholar
  22. 22.
    Pal, A. (2015). Effect of different load models on the three-sample based quadratic prediction algorithm. In Proceedings of the IEEE Power Engineering Society Conference Innovative Smart Grid Technologies, Washington D.C, 18–20 February 2015, pp 1–5.Google Scholar
  23. 23.
    Meditch, J. S. (1969). Stochastic optimal linear estimation and control. NY: McGraw-Hill Book Company.zbMATHGoogle Scholar
  24. 24.
    Moore, J. B. (1973). Discrete-time fixed-lag smoothing algorithms. IFAC-Automatica, 9, 163–173.zbMATHCrossRefGoogle Scholar
  25. 25.
    Grewal, M. S., & Andrews, A. P. (2015). Optimal smoothers. In Kalman Filtering: Theory and Practice with MATLAB, Chapter 6, Fourth Edition, John Wiley & Sons-IEEE Press, pp. 239–279.Google Scholar
  26. 26.
    Jones, K. D., Pal, A., & Thorp, J. S. (2015). Methodology for performing synchrophasor data conditioning and validation. IEEE Transactions on Power Systems, 30(3), 1121–1130.CrossRefGoogle Scholar
  27. 27.
    Jones, K. D. (2013). Synchrophasor-only dynamic state estimation & data conditioning. Ph.D. Dissertation, Virginia Tech.Google Scholar
  28. 28.
    Debs, A. S., & Larson, R. (1970). A dynamic estimator for tracking the state of a power system. IEEE Transactions PAS, 89(7), 1670–1678.CrossRefGoogle Scholar
  29. 29.
    Yang, T., Sun, H., & Bose, A. (2011). Transition to a two-level linear state estimator–Part I: Architecture. IEEE Transactions on Power Systems, 26(1), 46–53.CrossRefGoogle Scholar
  30. 30.
    Yang, T., Sun, H., & Bose, A. (2011). Transition to a two-level linear state estimator–Part II: Algorithm. IEEE Transactions on Power Systems, 26(1), 54–62.CrossRefGoogle Scholar
  31. 31.
    Zhang, L., Bose, A., Jampala, A., Madani, V., & Giri, J. (2016). Design, testing, and implementation of a linear state estimator in a real power system, to appear in IEEE Trans on Smart Grid, pp. 1–8.Google Scholar
  32. 32.
    Vanfretti, L., Chow, J. H., Sarawgi, S., & Fardanesh, B. (2011). A phasor-data-based state estimator incorporating phase bias correction. IEEE Transactions on Power Systems, 26(1), 111–119.CrossRefGoogle Scholar
  33. 33.
    Fernandes, E. R., Ghiocel, S. G., Chow, J. H., Ilse, D. E., Tran, D. D., & Zhang, Q. (2016). Application of a phasor-only state estimator to a large power system using real PMU data, to appear in IEEE Trans on Power Systems, pp. 1–9. Google Scholar
  34. 34.
    Gao, P., Wang, M., Ghiocel, S. G., Chow, J. H., Fardanesh, B., & Stefopoulos, G. (2016). Missing data recovery by exploiting low-dimensionality in power system synchrophasor measurements. IEEE Transactions on Power Systems, 31(2), 1006–1013.CrossRefGoogle Scholar
  35. 35.
    Meier, A. V., Culler, D., McEachern, A., & Arghandeh, R. (2014). Micro-synchrophasors for distribution systems. In Proceedings of the IEEE Power Engineering Society Conference Innovative Smart Grid Technologies, Washington D.C., 19–22 February 2014, pp. 1–5.Google Scholar
  36. 36.
    Sarri, S., Pignati, M., Romano, P., Zanni, L., & Paolone, M. (2015). A hardware-in-the-loop test platform for the performance assessment of a PMU-based real-time state estimator for active distribution networks. In Proceedings of the 2015 IEEE Eindhoven PowerTech, Eindhoven, Netherlands, June 29-July 2 2015, pp. 1–6.Google Scholar
  37. 37.
    Pignati, M., Zanni, L., Sarri, S., Cherkaoui, R., Le Boudec, J. Y., & Paolone, M. (2014). A pre-estimation filtering process of bad data for linear power systems state estimators using PMUs. In Proceedings of the Power Systems Computation Conference (PSCC), Wroclaw, Poland, 18–22 August 2014, pp. 1–8.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringVirginia TechBlacksburgUSA

Personalised recommendations