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Micro and Nanosystems

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ISSN (Print): 1876-4029
ISSN (Online): 1876-4037

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Research Article

M-estimation based Robust Approach for Hybrid Dynamic State Estimation in Power Systems

Author(s): Shubhrajyoti Kundu*, Mehebub Alam, Biman Kumar Saha Roy and Siddhartha Sankar Thakur

Volume 14, Issue 4, 2022

Published on: 12 May, 2022

Page: [358 - 368] Pages: 11

DOI: 10.2174/1876402914666220225161505

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Abstract

Background: The state estimation (SE) process in power systems estimates bus voltage magnitude and phase angles vital for operating the system securely and reliably. The power systems state estimation problem has been extensively solved through a weighted least squares (WLS) based static approach that fails to track the system dynamics. Furthermore, those approaches are not inherently robust against outliers, yielding a separate bad data processing (BDP) technique. Popular Dynamic state estimation (DSE) schemes which mainly employ nonlinear Kalman filters like Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), also suffer from providing a reasonable estimation of states in the presence of bad data. Although several generalized maximum (GM) likelihood- based DSE approaches are robust against outliers, they are mainly based on nonlinear Kalman filters, which yield an iterative process. Therefore, this article focuses on developing a robust DSE approach that gives a good estimation of states against outliers in a single iteration.

Objective: This article aims to propose a robust hybrid non-iterative DSE approach that gives robust SE results in the presence of bad data.

Methods: The proposed novel robust hybrid DSE (NRHDSE) approach combines the robust Mestimation with the original novel hybrid DSE (NHDSE) approach. The proposed scheme implements a suitable linear relationship between integrated hybrid measurements and complex states. The proposed method uses a linear measurement model and thus employs an optimal linear Kalman filter to correct or estimate states.

Results: The efficacy of the proposed approach has been demonstrated by applying it on IEEE 57, 118 bus test systems and one more extensive 246 Indian utility bus system, namely, Northern Regional Power Grid (NRPG), and after that comparing it with the original NHDSE, and DSE methods based on traditional EKF and M-estimation based robust version of EKF (REKF). The simulation result demonstrates the superiority of the proposed approach.

Conclusion: Obtained results clearly show the superiority of the proposed approach.

Keywords: M-estimation, robust, NRHDSE, NHDSE, PMUs, Linear Kalman filter.

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[1]
Schweppe, F.C.; Wildes, J. Power system static state estimation, Part I-Part III. IEEE Trans. Power Apparatus Syst, 1970, 89(1), 120-135.
[http://dx.doi.org/10.1109/TPAS.1970.292678]
[2]
Phadke, A.G. Synchronized phasor measurements in power systems. IEEE Comput. Appl. Power, 1993, 6(2), 10-15.
[http://dx.doi.org/10.1109/67.207465]
[3]
Zhou, M.; Centeno, V.A.; Thorp, J.S.; Phadke, A.G. An alternative for including phasor measurements in state estimators. IEEE Trans. Power Syst., 2006, 21(4), 1930-1937.
[http://dx.doi.org/10.1109/TPWRS.2006.881112]
[4]
Asprou, M.; Chakrabarty, S.; Kyriakides, E. A two-stage state estimator for dynamic monitoring of power systems. IEEE Syst. J., 2017, 11(3), 1767-1776.
[http://dx.doi.org/10.1109/JSYST.2014.2375951]
[5]
Manousakis, N.M.; Korres, G.N. A hybrid power system state estimator using synchronized and unsynchronized sensors. Int. Transac. Electr. Eng. Sys., 2018, 28(8), e2580.
[http://dx.doi.org/10.1002/etep.2580]
[6]
Nuqui, R.; Phadke, A.G. Hybrid linear state estimator utilizing synchronized phasor measurements. IEEE Lausanne Power Tech, 1-5 July 2007 Lausanne, Switzerland. Available from:. www.ieeexplore.ieee.org/abstract
[7]
Gol, M.; Abur, A. A hybrid state estimator for systems with limited number of PMUs. IEEE Trans. Power Syst., 2014, 30(3), 1511-1517.
[http://dx.doi.org/10.1109/TPWRS.2014.2344012]
[8]
Kirincic, V.; Skok, S.; Terziza, V. A two step hybrid power system state estimator. Int. Trans. Electr. Energy Syst., 2015, 25(7), 1158-1172.
[http://dx.doi.org/10.1002/etep.1894]
[9]
Mandal, J.K.; Sinha, A.K.; Roy, L. measurement function in power system dynamic state estimation. IEE Proc., Gener. Transm. Distrib., 1995, 142(3), 289-296.
[http://dx.doi.org/10.1049/ip-gtd:19951715]
[10]
Valverde, G.; Terziza, V. Unscented Kalman filter for power system dynamic state estimation. IET Gener. Transm. Distrib., 2011, 5(1), 29-37.
[http://dx.doi.org/10.1049/iet-gtd.2010.0210]
[11]
Wang, S.; Gao, W.; Meliopoulos, A.S. An alternative method for power system dynamic state estimation based on unscented transform. IEEE Trans. Power Syst., 2012, 27(2), 942-950.
[http://dx.doi.org/10.1109/TPWRS.2011.2175255]
[12]
Kundu, S.; Kumar, A.; Alam, M.; Roy, B.K.; Thakur, S.S. Unscented Kalman Filter Based Dynamic State Estimation in Power systems using complex synchronized PMU measurements.Control Applications in Modern Power systems; Springer: Singapore, 2021, pp. 99-110.
[http://dx.doi.org/10.1007/978-981-15-8815-0_9]
[13]
Arasaratnam, I.; Haykin, S. Cubature Kalman Filters. IEEE Trans. Automat. Contr., 2009, 54(6), 1254-1269.
[http://dx.doi.org/10.1109/TAC.2009.2019800]
[14]
Sharma, A.; Srivastava, S.C.; Chakrabarty, S. A cubature Kalman Filter based power system dynamic state estimator. IEEE Trans. Instrum. Meas., 2017, 66(8), 2036-2045.
[http://dx.doi.org/10.1109/TIM.2017.2677698]
[15]
Kundu, S.; Alam, M.; Roy, B.K.S.; Thakur, S.S. A non-iterative hybrid dynamic state estimation scheme utilizing PMU and SCADA meas-urements. Devices Integr. Circuit, 2021, 2021, 185-189.
[16]
Nishiya, K.; Hasegawa, J.; Koike, T. Dynamic state estimation including anomaly detection and identification for power system. IEE Proc. C-Gener. Trans. Distr.,, 1982, 129(5), pp. 192-198.
[17]
Da Silva, A.M.L.; Filho, M.B.; Do, C.; Cantera, J.M.C. An efficient dynamic state estimation algorithm including bad data processing. IEEE Power Eng. Rev., 1987, 7(11), 5526913.
[http://dx.doi.org/10.1109/MPER.1987.5526913]
[18]
Zhao, J.B.; Zhang, G.X.; Scala, M.L. PMU based robust dynamic state estimation method in power systems. Proceedings of the IEEE Power Energy Society General meeting, 2015, pp. 26-30. July; Denver, CO, USA.
[19]
Zhao, J.; Zhang, G.; Das, K.; Korres, G.N.; Manousakis, N.M.; Sinha, A.K.; He, Z. Power systems Real Time monitoring by using PMU based state estimation method. IEEE Trans. Smart Grid, 2016, 7(1), 300-309.
[http://dx.doi.org/10.1109/TSG.2015.2431693]
[20]
Zhao, J.; Zhang, G.; Dong, Z.Y.; La Scala, M. Robust forecasting aided power system state estimation considering state correlations. IEEE Trans. Smart Grid, 2018, 9(4), 2658-2666.
[http://dx.doi.org/10.1109/TSG.2016.2615473]
[21]
Zhao, J.; Netto, M.; Mili, L. A robust iterated extended kalman filter for power system dynamic state estimation. IEEE Trans. Power Syst., 2017, 32(4), 3205-3216.
[http://dx.doi.org/10.1109/TPWRS.2016.2628344]
[22]
Zhao, J.; Mili, L. A robust generalized- maximum likelihood unscented kalman filter for power system dynamic state estimation. IEEE J. Sel. Top. Signal Process., 2018, 12(4), 246-255.
[http://dx.doi.org/10.1109/JSTSP.2018.2827261]
[23]
Jin, Z.; Zhao, J.; Chakrabarty, S.; Ding, L.; Terzija, V. A hybrid robust forecasting- aided state estimator considering bimodal Gaussian mixture measurement errors. Int. J. Electr. Power Energy Syst., 2020, 120, 105962.
[http://dx.doi.org/10.1016/j.ijepes.2020.105962]
[24]
Jin, Z.; Chakrabarty, S.; Yu, J.; Ding, L.; Terzija, V. An improved algorithm for cubature Kalman filter-based forecasting aided state esti-mation and anomaly detection. Int. Trans. Electr. Energy Syst., 2021, 2021, e12714.
[25]
Moshtagh, S.; Rahmani, M. Robust hybrid state estimation for power systems utilizing Phasor measurement units. Electr. Power Syst. Res., 2021, 2021, 107195.
[http://dx.doi.org/10.1016/j.epsr.2021.107195]
[26]
Dubey, A.; Chakrabarty, S.; Terzija, V. SCADA and PMU Measurement based methods for Robust Hybrid state estimation. Electr. Power Compon. Syst., 2019, 47(9-10), 849-860.
[http://dx.doi.org/10.1080/15325008.2019.1627606]
[27]
Huber, P.J. Robust Statistics; Wiley: New York, 2nd ed.. , 1981, p. 384.
[http://dx.doi.org/10.1002/0471725250]
[28]
Li, Y.; Li, J.; Qi, J.; Chen, L. Robust cubature kalman filter for dynamic state estimation of synchronous machines under unknown meas-urement noise statistics. IEEE Access, 2019, 7, 29139-29148.
[http://dx.doi.org/10.1109/ACCESS.2019.2900228]
[29]
Grigg, C. The IEEE reliability test system- 1996. A report prepared by reliability test system task force of the application of probability methods subcommittee. IEEE Trans. Power Syst., 1999, 14(3), 1010-1020.
[http://dx.doi.org/10.1109/59.780914]

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