IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i23p7847-d685635.html
   My bibliography  Save this article

Resiliency Improvement of an AC/DC Power Grid with Embedded LCC-HVDC Using Robust Power System State Estimation

Author

Listed:
  • Abdulwahab A. Aljabrine

    (ECE-Department, University of Idaho, Moscow, ID 83843, USA
    These authors contributed equally to this work.)

  • Abdallah A. Smadi

    (ECE-Department, University of Idaho, Moscow, ID 83843, USA
    These authors contributed equally to this work.)

  • Yacine Chakhchoukh

    (ECE-Department, University of Idaho, Moscow, ID 83843, USA)

  • Brian K. Johnson

    (ECE-Department, University of Idaho, Moscow, ID 83843, USA)

  • Hangtian Lei

    (ECE-Department, University of Idaho, Moscow, ID 83843, USA)

Abstract

The growth of renewable energy generation in the power grid brings attention to high-voltage direct current (HVDC) transmission as a valuable solution for stabilizing the system. Robust hybrid power system state estimation could enhance the resilience of the control of these systems. This paper proposes a two-stage, highly robust least-trimmed squares (LTS)-based estimator. The first step combines the supervisory control and data acquisition (SCADA) measurements using the robust LTS-based estimator. The second step merges the obtained state results with the available phasor measurement units (PMUs) measurements using a robust Huber M-estimator. The proposed robust LTS-based estimator shows good performance in the presence of Gaussian measurement noise. The proposed estimator is shown to resist and correct the effect of false data injection (FDI) attacks and random errors on the measurement vector and the Jacobian matrix. The state estimation (SE) is executed on a modified version of the CIGRE bipole LCC-HVDC benchmark model integrated into the IEEE 12-bus AC dynamic test system. The obtained simulation results confirm the effectiveness and robustness of the proposed two-stage LTS-based SE.

Suggested Citation

  • Abdulwahab A. Aljabrine & Abdallah A. Smadi & Yacine Chakhchoukh & Brian K. Johnson & Hangtian Lei, 2021. "Resiliency Improvement of an AC/DC Power Grid with Embedded LCC-HVDC Using Robust Power System State Estimation," Energies, MDPI, vol. 14(23), pages 1-17, November.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7847-:d:685635
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/23/7847/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/23/7847/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Motaz Ayiad & Helder Leite & Hugo Martins, 2020. "State Estimation for Hybrid VSC Based HVDC/AC Transmission Networks," Energies, MDPI, vol. 13(18), pages 1-27, September.
    2. Agulló, Jose & Croux, Christophe & Van Aelst, Stefan, 2008. "The multivariate least-trimmed squares estimator," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 311-338, March.
    3. Gaurav Kumar Roy & Marco Pau & Ferdinanda Ponci & Antonello Monti, 2021. "A Two-Step State Estimation Algorithm for Hybrid AC-DC Distribution Grids," Energies, MDPI, vol. 14(7), pages 1-21, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gaurav Kumar Roy & Marco Pau & Ferdinanda Ponci & Antonello Monti, 2021. "A Two-Step State Estimation Algorithm for Hybrid AC-DC Distribution Grids," Energies, MDPI, vol. 14(7), pages 1-21, April.
    2. Roelant, E. & Van Aelst, S. & Croux, C., 2009. "Multivariate generalized S-estimators," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 876-887, May.
    3. Laniado Rodas, Henry, 2019. "Shrinkage reweighted regression," DES - Working Papers. Statistics and Econometrics. WS 28500, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Hill, Jonathan B. & Aguilar, Mike, 2013. "Moment condition tests for heavy tailed time series," Journal of Econometrics, Elsevier, vol. 172(2), pages 255-274.
    5. Ella Roelant & Stefan Aelst & Gert Willems, 2009. "The minimum weighted covariance determinant estimator," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(2), pages 177-204, September.
    6. Lanius, Vivian & Gather, Ursula, 2010. "Robust online signal extraction from multivariate time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 966-975, April.
    7. Kudraszow, Nadia L. & Maronna, Ricardo A., 2011. "Estimates of MM type for the multivariate linear model," Journal of Multivariate Analysis, Elsevier, vol. 102(9), pages 1280-1292, October.
    8. Le Chang & Yanlin Shi, 2024. "A discussion on the robust vector autoregressive models: novel evidence from safe haven assets," Annals of Operations Research, Springer, vol. 339(3), pages 1725-1755, August.
    9. Motaz Ayiad & Emily Maggioli & Helder Leite & Hugo Martins, 2021. "Communication Requirements for a Hybrid VSC Based HVDC/AC Transmission Networks State Estimation," Energies, MDPI, vol. 14(4), pages 1-25, February.
    10. Paindaveine, Davy & Van Bever, Germain, 2014. "Inference on the shape of elliptical distributions based on the MCD," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 125-144.
    11. Van Aelst, Stefan & Willems, Gert & Zamar, Ruben H., 2013. "Robust and efficient estimation of the residual scale in linear regression," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 278-296.
    12. Cator, Eric A. & Lopuhaä, Hendrik P., 2010. "Asymptotic expansion of the minimum covariance determinant estimators," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2372-2388, November.
    13. Hofmann, Marc & Kontoghiorghes, Erricos John, 2010. "Matrix strategies for computing the least trimmed squares estimation of the general linear and SUR models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3392-3403, December.
    14. Li, Dan & Drovandi, Christopher & Clements, Adam, 2024. "Outlier-robust methods for forecasting realized covariance matrices," International Journal of Forecasting, Elsevier, vol. 40(1), pages 392-408.
    15. Davy Paindaveine & Germain Van Bever, 2013. "Inference on the Shape of Elliptical Distribution Based on the MCD," Working Papers ECARES ECARES 2013-27, ULB -- Universite Libre de Bruxelles.
    16. Rodrigo Puentes & Carolina Marchant & Víctor Leiva & Jorge I. Figueroa-Zúñiga & Fabrizio Ruggeri, 2021. "Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model," Mathematics, MDPI, vol. 9(6), pages 1-24, March.
    17. Joseph Cooper & Carl Zulauf & Michael Langemeier & Gary Schnitkey, 2012. "Implications of within county yield heterogeneity for modeling crop insurance premiums," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 72(1), pages 134-155, May.
    18. Gottard, Anna & Pacillo, Simona, 2010. "Robust concentration graph model selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3070-3079, December.
    19. Serneels, Sven & Verdonck, Tim, 2009. "Principal component regression for data containing outliers and missing elements," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3855-3863, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7847-:d:685635. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.