http://iet.metastore.ingenta.com
1887

Hierarchical parallel dynamic estimator of states for interconnected power system

Hierarchical parallel dynamic estimator of states for interconnected power system

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Real-time visualisation of large power systems, by tracking the system states, is a challenging task as it involves processing a large measurement set to obtain the system states. This study proposes a hierarchical parallel dynamic estimation algorithm to estimate the states of a large-scale interconnected power system. The power system is decomposed into smaller subsystems, which is processed in parallel to obtain a reduced order state estimate. This information is then transmitted to the central processor, which collates the individual reduced order estimates to obtain the global estimates. Each processor uses state matrix of smaller dimension, thereby reducing the computational burden. The low-level processors utilise only a fraction of the global measurements in the proposed approach, and there is no need for any information exchange from the central processor to the low level processors, which helps in reducing the communication requirements. Moreover, detection of anomalies can also be carried out at the local processors without the need for any separate bad data detection at the central processor. IEEE 30- and 118-bus systems are used as test beds to study the proposed approach.

References

    1. 1)
      • 1. Abur, A., Exposito, A.G.: ‘Power system state estimation: theory and implementation’ (Mercel Dekker, New York, NY, USA, 2004, 1st edn.).
    2. 2)
      • 2. Monticelli, A.: ‘State estimation in electric power systems: a generalized approach’ (Kluwer Academic Publishers, Boston, MA, USA, 1999, 1st edn.).
    3. 3)
      • 3. Schweppe, F.C., Wildes, J.: ‘Power system static-state estimation, Part I, II, III’, IEEE Trans. Power Appl. Syst., 1970, 89, (1), pp. 120135.
    4. 4)
      • 4. Do Coutto Filho, M.B., de Souza, J.C.S.: ‘Forecasting-aided state estimation – Part I: panorama’, IEEE Trans. Power Syst., 2009, 24, (4), pp. 16671677.
    5. 5)
      • 5. Leite da Silva, A., Do Coutto Filho, M., de Queiroz, J.: ‘State forecasting in electric power systems’, IEE Proc. Gener. Transm. Distrib., 1983, 130, (5), pp. 237244.
    6. 6)
      • 6. Singh, A., Pal, B.: ‘Decentralized dynamic state estimation in power systems using unscented transformation’, IEEE Trans. Power Syst., 2014, 29, (2), pp. 794804.
    7. 7)
      • 7. Simon, D.: ‘Optimal state estimation: Kalman, H infinity, and nonlinear approaches’ (Wiley, Hoboken, NJ, USA, 2006, 1st edn.).
    8. 8)
      • 8. Valverde, G., Terzija, V.: ‘Unscented Kalman filter for power system dynamic state estimation’, IET Gener. Transm. Distrib., 2011, 5, (1), pp. 2937.
    9. 9)
      • 9. Rousseaux, P., Van Cutsem, T., Liacco, T.D.: ‘Whither dynamic state estimation?Int. J. Electr. Power Energy Syst., 1990, 12, (2), pp. 104116.
    10. 10)
      • 10. Gomez-Exposito, A., de la Villa Jaen, A., Gomez-Quiles, C., et al: ‘A taxonomy of multi-area state estimation methods’, Electr. Power Syst. Res., 2011, 81, (4), pp. 10601069.
    11. 11)
      • 11. Zhao, L., Abur, A.: ‘Multi area state estimation using synchronized phasor measurements’, IEEE Trans. Power Syst., 2005, 20, (2), pp. 611617.
    12. 12)
      • 12. Rousseaux, P., Mallieu, D., Van Cutsem, T., et al: ‘Dynamic state prediction and hierarchical filtering for power system state estimation’, Automatica, 1988, 24, (5), pp. 595618.
    13. 13)
      • 13. Van Cutsem, T., Horward, J.L., Ribbens-Pavella, M.: ‘A two-level static state estimator for electric power systems’, IEEE Trans. Power Appl. Syst., 1981, PAS-100, (8), pp. 37223732.
    14. 14)
      • 14. Sinha, A.K., Mandal, J.: ‘Hierarchical dynamic state estimator using ANN-based dynamic load prediction’, IEE Proc. Gener. Transm. Distrib., 1999, 146, (6), pp. 541549.
    15. 15)
      • 15. Sharma, A., Srivastava, S.C., Chakrabarti, S.: ‘Multi-agent-based dynamic state estimator for multi-area power system’, IET Gener. Transm. Distrib., 2016, 10, (1), pp. 131141.
    16. 16)
      • 16. Karimipour, H., Dinahavi, V.: ‘Extended Kalman filter-based parallel dynamic state estimation’, IEEE Trans. Smart Grid., 2015, 6, (3), pp. 15391549.
    17. 17)
      • 17. Guo, Z., Li, S., Wang, X., et al: ‘Distributed point-based Gaussian approximation filtering for forecasting-aided state estimation in power systems’, IEEE Trans. Power Syst., 2016, 31, (4), pp. 25972608.
    18. 18)
      • 18. Roy, S., Hashemi, R., Laub, A.: ‘Square root parallel Kalman filtering using reduced-order local filters’, IEEE Trans. Aerosp. Electron. Syst., 1991, 27, (2), pp. 276289.
    19. 19)
      • 19. Sharma, A., Srivastava, S.C., Chakrabarti, S.: ‘An iterative multiarea state estimation approach using area slack bus adjustment’, IEEE Syst. J., 2016, 10, (1), pp. 6977.
    20. 20)
      • 20. Sreenath, J.G., Chakrabarti, S., Sharma, A.: ‘Implementation of Rauch Tung Striebel smoother for power system dynamic state estimation in the presence of PMU measurements’, Innov. Smart Grid Technology, Asia, Bangkok, Thailand, November 2015, pp. 16.
    21. 21)
      • 21. MATLAB. version 7.13.0.564(R2011b). Available at https://in.mathworks.com/products/matlab, accessed 27 December 2016.
    22. 22)
      • 22. ‘IEEE 14, 30, and, 118 bus systems’. Available at http://www.ee.washington.edu/research/pstca, accessed 27 December 2016.
    23. 23)
      • 23. Leite da Silva, A., Do Coutto Filho, M., Cantera, J.: ‘An efficient dynamic state estimation algorithm including bad data processing’, IEEE Trans. Power Syst., 1987, 2, (4), pp. 10501058.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2017.1733
Loading

Related content

content/journals/10.1049/iet-gtd.2017.1733
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address