STATE ESTIMATION OF POWER SYSTEM MODE PARAMETERS BY TELEMETRY AND SYNCHRONIZED PHASOR MEASUREMENTS
Authors
DOI:
https://doi.org/10.31489/2024No4/61-70Keywords:
state estimation, Gauss-Newton method, extended Kalman filter, telemetry, synchronized phasor measurementsAbstract
Real time hardware and software systems are operated at centers of a power system operation. The key unit of these systems is the state estimation block since, based on the results of mode parameters derived from this block, parameters that are more comprehensive can be calculated. These parameters are considered for system stability and reliability. Currently, not only telemetry but also synchronized phasor measurements can be used for a state estimation. Therefore, the development of state estimation methods is the relevant task. The proposed method allows improving the estimation accuracy and the quality of decisions, related to system stability and reliability. The method is based on mathematical frameworks of the Gauss-Newton method and extended Kalman filter, when telemetry and synchronized phasor measurements arrays are used simultaneously. It is confirmed, that the given method increases an accuracy of the voltage and active power flow estimation at steady state and post-accident modes, in contrast to the standard state estimation method. The developed algorithm enables the implementation of this method into the state estimation block of real time hardware and software systems. The upcoming trends for the development of state estimation methods in the event of dynamic processes in power system areas are also formed.
References
Hoseinzadeh B., Bak C.L. (2018) Centralized coordination of emergency control and protection system using on-line outage sensivity index. Electric Power Systems Research, 163, 413 – 422. DOI:10.1016/j.epsr.2018.07.016. DOI: https://doi.org/10.1016/j.epsr.2018.07.016
Panteli M., Crossley P.A., Fitch J. (2015) Design of dependable and secure system integrity protection scheme. Electrical Power and Energy Systems, 68, 15 – 25. DOI:10.1016/j.ijepes.2014.12.047. DOI: https://doi.org/10.1016/j.ijepes.2014.12.047
Ballal M.S., Kulkarni A.R., Suryawanshi H.M. (2021) Methodology for the improvements in synchrophasor based System Integrity Protection Schemes under stressed conditions. Sustainable Energy, Grids and Networks, 26, 1 – 19. DOI:10.1016/j.segan.2021.100465. DOI: https://doi.org/10.1016/j.segan.2021.100465
Rao A.K., Kundu P. (2023) System integrity protection scheme for minimizing curtailment considering transmission line thermal limits. Sustainable Energy, Grids and Networks, 33, 1 – 13. DOI:10.1016/j.segan.2022.100970. DOI: https://doi.org/10.1016/j.segan.2022.100970
Kotha S.K., Rajpathak B., Mallareddy M., Bhuvanagiri R. (2023) Wide area measurement systems based Power System State Estimation using a Robust Linear-Weighted Least Square method. Energy Reports, 9, 23 – 32. DOI:10.1016/j.egyr.2023.05.046. DOI: https://doi.org/10.1016/j.egyr.2023.05.046
Khalid H.M., Flitti F., Mahmoud M.S., Hamdan M.M., Muyeen S.M., Dong Z.Y. (2023) Wide area monitoring system operations in modern power grids: A median regression function-based state estimation approach towards cyber attacks. Sustainable Energy, Grids and Networks, 34, 1 – 15. DOI:10.1016/j.segan.2023.101009. DOI: https://doi.org/10.1016/j.segan.2023.101009
Phadke G., Thorp J. S. (2008) Synchronized Phasor Measurements and Their Applications. New York: Springer, 247. Available at: https://ieeexplore.ieee.org/document/5447627. DOI: https://doi.org/10.1007/978-0-387-76537-2
Martin K.E. (2015) Synchrophasor measurements under the IEEE Standard C37. 118.1-2011 with amendment C37.118.1a. IEEE Transactions on Power Delivery, 30, 3, 1 – 14. Available at: https://ieeexplore.ieee.org/abstract/document/7052413. DOI: https://doi.org/10.1109/TPWRD.2015.2403591
Espejo E.B., Sevilla F.R.S., Korba P. (2023) Monitoring and Control of Electrical Power Systems Using Machine Learning Techniques. Elsevier, 339. Available at: https://www.sciencedirect.com/book/ 9780323999045/monitoring-and-control-of-electrical-power-systems-using-machine-learning-techniques.
Karvelis G.I., Korres G.N., Darmis O.A. (2022) State estimation using SCADA and PMU measurements for networks containing classic HVDC links. Electric Power Systems Research, 212, 2–7. DOI:10.1016/j.epsr.2022.108544. DOI: https://doi.org/10.1016/j.epsr.2022.108544
Zhenjie W., Lin Y., Pei L., Chengda L., Zhengyang Z., Suirong L., Sun J.F. (2021) State estimation of distribution network based on hybrid measurement combined with multi-source asynchronous data. Energy Reports, 8, 1778–1783. DOI:10.1016/j.egyr.2022.03.195. DOI: https://doi.org/10.1016/j.egyr.2022.03.195
Liu Y., Lin Y., Yue K. (2021) Modern power system state estimation methods. Elsevier, 277. Available at: https://nyuscholars.nyu.edu/en/publications/modern-power-system-state-estimation-methods.
Li K., Han X. (2022) A distributed Gauss–Newton method for distribution system state estimation. International Journal of Electrical Power and Energy Systems, 136, 1 – 14. DOI:10.1016/j.ijepes.2021.107694. DOI: https://doi.org/10.1016/j.ijepes.2021.107694
Ahmad F., Rashid M.A.K., Rasool A., Özsoy E.E., Sabanovic A., Elitaş M. (2017) Performance Comparison of Static and Dynamic State Estimators for Electric Distribution Systems. International Journal of Emerging Electric Power Systems, 18, 3, 1 – 14. DOI:10.1515/ijeeps-2016-0299. DOI: https://doi.org/10.1515/ijeeps-2016-0299
Zhao J., Gomez-Exposito A., Netto M., Mili L., Abur A., Terzija V., Kamwa I., Pal B., Singh A.K., Qi J., Huang Z., Meliopoulos A.S. (2019) Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work. IEEE Transactions on Power Systems, 34, 4, 3188 – 3198. DOI:10.1109/TPWRS.2019.2894769. DOI: https://doi.org/10.1109/TPWRS.2019.2894769
Yu Y., Li Q., Chen C., Zheng X., Tan Y. (2022) Improved dynamic state estimation of power system using unscented Kalman filter with more accurate prediction model. Energy Reports, 8, 364 – 376. DOI:10.1016/j.egyr.2022.10.112. DOI: https://doi.org/10.1016/j.egyr.2022.10.112
Hong-de D., Shao-wu D., Yuan-cai C., Guang-bin W. (2012) Performance comparison of EKF/UKF/CKF for the tracking of ballistic target. TELKOMNIKA Indonesian Journal of Electrical Engineering, 10, 7, 1692 – 1699. DOI: 10.11591/telkomnika.v10i7.1564. DOI: https://doi.org/10.11591/telkomnika.v10i7.1564
Mokhtari S., Yen K.K. (2023) Dynamic state estimation with additive noise for load frequency control using bilateral fuzzy adaptive unscented Kalman filter. Electric Power Systems Research, 220, 1 – 10. DOI: 10.1016/j.epsr.2023.109363. DOI: https://doi.org/10.1016/j.epsr.2023.109363
Lorenz-Meyer N., Suchantke R., Schiffer J. (2023) Dynamic state and parameter estimation in multi-machine power systems. Experimental demonstration using real-world PMU-measurements. Control Engineering Practice, 135, 1 – 10. DOI: 10.1016/j.conengprac.2023.105491. DOI: https://doi.org/10.1016/j.conengprac.2023.105491
Batseva N.L., Foos Y.A. (2023) Effectiveness of the dynamic state estimation method application for mode parameters of a power system. Bulletin of the Ivanovo State Power Engineering University, 3, 5 – 15. DOI:10.17588/2072-2672.2023.3.005-015. [in Russian] DOI: https://doi.org/10.17588/2072-2672.2023.3.005-015
Veerakumar N., Cetenovic D., Kongurai K., Popov M., Jongepier A., Terzija V. (2023) PMU-based real-time distribution system state estimation considering anomaly detection, discrimination and identification. International Jornal of Electrical Power and Energy Systems, 148, 1 – 15. DOI: 10.1016/j.ijepes.2022.108916. DOI: https://doi.org/10.1016/j.ijepes.2022.108916
Pugachev V.S., Sinitsin I.N. (2004) A theory of stochastic systems. Moscow, Logos Publ., 999. Available at: https://search.rsl.ru/ru/record/01002457901. [in Russian]
Downloads
Received
Revised
Accepted
Published online
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
