access icon free Generalised-fast decoupled state estimator

© The Institution of Engineering and Technology
Received 12/03/2018, Accepted 30/08/2018, Revised 15/06/2018, Published 05/09/2018

Nowadays the fast-decoupled state estimation (FDSE) is widely used in almost every power system control centre. FDSE is effective and efficient for most transmission systems but it may not converge for systems with a large ratio of branch resistance to reactance (R/X); meanwhile the branch current magnitude measurements (BCMMs) cannot be reliably used in FDSE, thereby limiting its applications especially for the distribution systems where BCMMs abound. In this study, the above two problems have been addressed by transforming all measurements so that they can be classified as quasi-real power measurements and quasi-reactive power measurements, leading to a generalised FDSE (GFDSE) with a solid theoretical foundation. The formulation of GFDSE is based on only the assumption, rather than three assumptions used in FDSE. As a result, GFDSE has good adaptability to transmission systems as well as distribution systems; additionally, BCMMs can be reliably used in GFDSE. Case studies based on IEEE benchmark systems and a real grid of China demonstrate that the proposed GFDSE has very good convergence properties for transmission systems and distribution systems; and at the same time, the proposed GFDSE is also superior to FDSE in terms of computational efficiency under almost all cases.

Inspec keywords: power system measurement; power system control; reactive power; power system state estimation; load flow

Other keywords: conventional FDSE; transmission systems; quasireal power measurements; GFDSE; power system control centre; branch resistance; generalised FDSE; generalised fast-decoupled state estimation; BCMMs; distribution systems; quasireactive power measurements; IEEE benchmark systems; branch current magnitude measurements

Subjects: Power transmission, distribution and supply; Power system control; Control of electric power systems; Power system measurement and metering

References

    1. 1)
      • 7. Alam, S.M.S., Natarajan, B., Pahwa, A.: ‘Distribution grid state estimation from compressed measurements’, IEEE Trans. Smart Grid, 2014, 5, (4), pp. 1631–1642.
    2. 2)
      • 25. Lin, W.M., Teng, J.H.: ‘Distribution fast decoupled state estimation by measurement pairing’, IEE Proc. Gener. Transm. Distrib., 1996, 143, (1), pp. 43–48.
    3. 3)
      • 1. Chen, Y., Ma, J., Zhang, P., et al: ‘Robust state estimator based on maximum exponential absolute value’, IEEE Trans. Smart Grid, 2017, 8, (4), pp. 1537–1544.
    4. 4)
      • 20. Wang, H., Schulz, N.N.: ‘A revised branch current based distribution system state estimation algorithm and meter placement impact’, IEEE Trans. Power Syst., 2004, 19, (1), pp. 207–213.
    5. 5)
      • 16. Schweppe, F.C.: ‘Power system static state estimation, part III: implementation’, IEEE Trans. Power Appl. Syst., 1970, PAS-89, (1), pp. 130–135.
    6. 6)
      • 30. Caro, E., Conejo, A.J., Minguez, R.: ‘Power system state estimation considering measurement dependencies’, IEEE Trans. Power Syst., 2009, 24, (4), pp. 1875–1885.
    7. 7)
      • 8. Singh, R., Pal, B.C., Jabr, R.A.: ‘Distribution system state estimation through Gaussian mixture model of the load as pseudo-measurement’, IET Gener. Transm. Distrib., 2010, 4, (1), pp. 50–59.
    8. 8)
      • 32. Zhao, J.B., Wang, S., Mili, L., et al: ‘A robust state estimation framework considering measurement correlations and imperfect synchronization’, IEEE Trans. Power Syst., 2018, 33, (4), pp. 4604–4613.
    9. 9)
      • 24. Habiballah, O., Quintana, V.H.: ‘Fast-decoupled rectangular co-ordinate state estimation with efficient data structure management’, IEE Proc. C, Gener. Transm. Distrib., 1991, 138, (5), pp. 462–468.
    10. 10)
      • 5. Haughton, D.A., Heydt, G.T.: ‘A linear state estimation formulation for smart distribution systems’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 1187–1195.
    11. 11)
      • 4. Aminifar, F., Fotuhi-Firuzabad, M., Safdarian, A., et al: ‘Synchrophasor measurement technology in power systems: panorama and state-of-the-art’, IEEE Access, 2015, 2, pp. 1607–1628.
    12. 12)
      • 11. Wu, J., He, Y., Jenkins, N.: ‘A robust state estimator for medium voltage distribution networks’, IEEE Trans. Power Syst., 2012, 28, (2), pp. 1008–1016.
    13. 13)
      • 3. Celli, G., Pegoraro, P.A., Pilo, F., et al: ‘DMS cyber physical simulation for assessing the impact of state estimation and communication media in smart grid operation’, IEEE Trans. Power Syst., 2014, 29, (5), pp. 2436–2446.
    14. 14)
      • 22. Horisberger, H.P., Richard, J.C., Rossier, C.: ‘A fast decoupled static state-estimator for electric power system’, IEEE Trans. Power Appl. Syst., 1976, 98, (1), pp. 208–215.
    15. 15)
      • 10. Manitsas, E., Singh, R., Pal, B.C., et al: ‘Distribution system state estimation using an artificial neural network approach for pseudo measurement modeling’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 1888–1896.
    16. 16)
      • 21. Pau, M., Pegoraro, P.A., Sulis, S.: ‘Efficient branch-current-based distribution system state estimation including synchronized measurements’, IEEE Trans. Instrum. Meas., 2013, 62, (9), pp. 2419–2429.
    17. 17)
      • 18. Li, K.: ‘State estimation for power distribution system and measurement impacts’, IEEE Trans. Power Syst., 1996, 11, (2), pp. 911–916.
    18. 18)
      • 9. Ghosh, A.K., Lubkeman, D.L., Jones, R.H.: ‘Load modeling for distribution circuit state estimation’, IEEE Trans. Power Deliv., 1997, 12, (2), pp. 999–1005.
    19. 19)
      • 2. Primadianto, A., Lu, C.N.: ‘A review on distribution system state estimation’, IEEE Trans. Power Syst., 2017, 32, (5), pp. 3875–3883.
    20. 20)
      • 19. Deng, Y., He, Y., Zhang, B.: ‘A branch estimation-based state estimation method for radial distribution systems’, IEEE Trans. Power Deliv., 2002, 17, (4), pp. 1057–1062.
    21. 21)
      • 27. Ju, Y., Wu, W., Ge, F., et al: ‘Fast decoupled state estimation for distribution networks considering branch ampere measurements’, IEEE Trans. Smart Grid, 2017, doi: 10.1109/TSG.2017.2709463.
    22. 22)
      • 6. Muscas, C., Pau, M., Pegoraro, P., et al: ‘Effects of measurements and pseudomeasurements correlation in distribution system state estimation’, IEEE Trans. Instrum. Meas., 2014, 63, (12), pp. 2813–2823.
    23. 23)
      • 29. Chen, Y., Liu, F., Mei, S., et al: ‘A robust WLAV state estimation using optimal transformations’, IEEE Trans. Power Syst., 2015, 30, (4), pp. 2190–2191.
    24. 24)
      • 14. Schweppe, F.C., Wildes, J.: ‘Power system static state estimation, part I: exact model’, IEEE Trans. Power Appl. Syst., 1970, PAS-89, (1), pp. 120–125.
    25. 25)
      • 26. Roy, L., Mohammed, T.A.: ‘Fast super decoupled state estimator for power systems’, IEEE Trans. Power Syst., 1997, 12, (4), pp. 1597–1603.
    26. 26)
      • 28. He, G., Dong, S., Qi, J., et al: ‘Robust state estimator based on maximum normal measurement rate’, IEEE Trans. Power Syst., 2011, 26, (4), pp. 2058–2065.
    27. 27)
      • 12. Hayes, B.P., Gruber, J.K., Prodanovic, M.: ‘A closed-loop state estimation tool for MV network monitoring and operation’, IEEE Trans. Smart Grid, 2014, 6, (4), pp. 2116–2125.
    28. 28)
      • 13. Lin, W.M., Teng, J.H.: ‘State estimation for distribution systems with zero-injection constraints’, IEEE Trans. Power Syst., 1996, 11, (1), pp. 518–524.
    29. 29)
      • 23. Garcia, A., Montielli, A., Abreu, P.: ‘Fast decoupled state estimation and bad data processing’, IEEE Trans. Power Appl. Syst., 1979, PAS-98, (5), pp. 1645–1652.
    30. 30)
      • 15. Schweppe, F.C., Rom, D.B.: ‘Power system static state estimation, part II: approximate model’, IEEE Trans. Power Appl. Syst., 1970, PAS-89, (1), pp. 125–130.
    31. 31)
      • 17. Abur, A., Expósito, A.G.: ‘Power system state estimation: theory and implementation’ (Marcel Dekker, New York, 2004), pp. 157–184.
    32. 32)
      • 31. Caro, E., Gustavo, V.: ‘Impact of transformer correlations in state estimation using the unscented transformation’, IEEE Trans. Power Syst., 2014, 29, (1), pp. 368–376.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2018.5076
Loading

Related content

content/journals/10.1049/iet-gtd.2018.5076
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading