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State estimation of synchronous generator mode parameters based on Kalman filter

https://doi.org/10.30724/1998-9903-2025-27-4-94-103

Abstract

Relevance. of the research is to develop a methodology for the Kalman filter tuning for the problem of synchronous generator control in the operation mode. This allows us to synthesize optimal and adaptive controllers. The complication consists in the absence of the possibility of direct measurement of all the necessary quantities.

The Purpose. To determine the approach of Kalman filter tuning for the control problems in the operating mode of a synchronous generator. To determine the filtering quality of the output signals and estimation of  the operating parameters with disturbance in the input channels and noise in the measuring ones.

Methods. To achieve the aim, the methods of mathematical modeling implemented by MatLab/Simulink tools were used. The methods of mathematical statistics and optimization were used.

Results. The paper describes the algorithm for Kalman filter tuning that allows to determine the vector of state variables without linearizing the synchronous generator model. The research was conducted on the influence of noise covariance matrix Q and R for quality filtration and parameters estimation of rotor angle and speed. For the research, a digital model with a synchronous generator from the MatLab/Simulink library was designed and another one was designed with a generator in the state space.

Conclusion. It has been established that matrices Q and R influence on the speed of movement of the estimated parameters by the true values. If the matrices components are equal the power spectral densities of the disturbances and noise in these measuring channels, the measurement error will tend for zero. The same result is obtained if the components of the Q and R matrices do not have the necessary power spectral densities of disturbances and noise, but at the same time, between the components of the Q and R matrices, it is necessary to maintain the same proportion as when they are created by the above-mentioned method.

About the Author

M. Yu. Frolov
Novosibirsk state technical university
Russian Federation

Mikhail Yu. Frolov

Novosibirsk



References

1. D. Luenberger, "An introduction to observers," in IEEE Transactions on Automatic Control, vol. 16, no. 6, pp. 596-602, December 1971, doi: 10.1109/TAC.1971.1099826

2. V. Radaisavljevic-Gajic and F. O. M. Safieh, "Discrete-Time Optimal Quadratic Performance Loss of a Linear Reduced Order Observer Based Controller Designed via a Sylvester Equation," 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Male, Maldives, 2024, pp. 1-6, doi: 10.1109/ICECCME62383.2024.10796489.P. V. S. Nag, C. S. Kumar, K. C. S. thampatty and T. B. Isha, "A modified approach for application of Augmented Extended Kalman filter for stator interturn fault diagnosis of a synchronous generator," 2018 4th International Conference on Electrical Energy Systems (ICEES), Chennai, India, 2018, pp. 465-469, doi: 10.1109/ICEES.2018.8442354.

3. J. Carvalho, K. Datta and Yoopyo Hong, "A new block algorithm for full -rank solution of the Sylvester-observer equation," in IEEE Transactions on Automatic Control, vol. 48, no. 12, pp. 2223-2228, Dec. 2003, doi: 10.1109/TAC.2003.820150.

4. K.-X. Cui and G.-R. Duan, "Adaptive Disturbance Observer Design for Discrete-Time High- Order Fully Actuated Systems Based on LMI and Its Application to Combined Spacecrafts," 2023 2nd Conference on Fully Actuated System Theory and Applications (CFASTA), Qingdao, China, 2023, pp. 12-17, doi: 10.1109/CFASTA57821.2023.10243313.

5. K.-X. Cui and G.-R. Duan, "Adaptive Disturbance Observer Design for Discrete-Time High- Order Fully Actuated Systems Based on LMI and Its Application to Combined Spacecrafts," 2023 2nd Conference on Fully Actuated System Theory and Applications (CFASTA), Qingdao, China, 2023, pp. 12-17, doi: 10.1109/CFASTA57821.2023.10243313

6. R. Molavi, K. Shojaee and D. A. Khaburi, "Optimal vector control of permanent magnet synchronous motor," 2008 IEEE 2nd International Power and Energy Conference, Johor Bahru, Malaysia, 2008, pp. 249-253, doi: 10.1109/PECON.2008.4762479.

7. M. Ai, Y. Sun and X. Lv, "Dynamic state estimation for synchronous machines based on interpolation H∞ extended Kalman filter," 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC), Nanjing, China, 2018, pp. 555-559, doi: 10.1109/YAC.2018.8406436.

8. L. Peng, L. Xie, F. Wu and C. Li, "Field-oriented control of synchronous motor based on adaptive extended Kalman filter," 2011 IEEE International Conference on Cloud Computing and Intelligence Systems, Beijing, China, 2011, pp. 633-637, doi: 10.1109/CCIS.2011.6045146.

9. P. S. Madhukar and L. B. Prasad, "State Estimation using Extended Kalman Filter and Unscented Kalman Filter," 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3), Lakshmangarh, India, 2020, pp. 1-4, doi: 10.1109/ICONC345789.2020.9117536.

10. A. I. Ilieş, G. Chindriş and D. Pitică, "A Comparison between State of Charge Estimation Methods: Extended Kalman Filter and Unscented Kalman Filter," 2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME), Pitesti, Romania, 2020, pp. 376-381, doi: 10.1109/SIITME50350.2020.9292232.

11. K. Kyslan, V. Šlapák, V. Petro, A. Marcinek and F. Ďurovský, "Speed Sensorless Control of PMSM with Unscented Kalman Filter and Initial Rotor Alignment," 2019 International Conference on Electrical Drives & Power Electronics (EDPE), The High Tatras, Slovakia, 2019, pp. 373-378, doi: 10.1109/EDPE.2019.8883918

12. Y. Li, J. Li, J. Qi and L. Chen, "Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics," in IEEE Access, vol. 7, pp. 29139-29148, 2019, doi: 10.1109/ACCESS.2019.2900228.

13. Huang, Jian & Ma, Zhixun. (2023). Sensorless Control of Synchronous Reluctance Machines Based on Improved Kalman Filter-Phase Locked Loop. 4602-4605. 10.1109/ICEMS59686.2023.10344934.

14. Y. Li, M. Aït-Ahmed, N. Aït-Ahmed, W. Shi and Z. Zhou, "LQR Control Speed and Voltage of Synchronous Alternator," 2010 International Conference on Intelligent Computation Technology and Automation, Changsha, China, 2010, pp. 617-620, doi: 10.1109/ICICTA.2010.503.

15. M. A. M. Cheema, J. E. Fletcher, D. Xiao and M. F. Rahman, "A Linear Quadratic Regulator - Based Optimal Direct Thrust Force Control of Linear Permanent-Magnet Synchronous Motor," in IEEETransactions on Industrial Electronics, vol. 63, no. 5, pp. 2722-2733, May 2016, doi: 10.1109/TIE.2016.2519331.

16. R. Molavi, K. Shojaee and D. A. Khaburi, "Optimal vector control of permanent magnet synchronous motor," 2008 IEEE 2nd International Power and Energy Conference, Johor Bahru, Malaysia, 2008, pp. 249-253, doi: 10.1109/PECON.2008.4762479.

17. f. r. badal, s. k. sarkar and s. k. das, "high performance ilqg controller design to enhance dynamic stability of multimachine power system," 2018 4th international conference on electrical engineering and information & communication technology (iceeict), dhaka, bangladesh, 2018, pp. 142-147, doi: 10.1109/ceeict.2018.8628063.

18. Obzor metodov opredeleniya parametrov modelej sinhronnyh generatorov / A. S. Berdin, A. N. Mojsejchenkov, P. Yu. Kovalenko [i dr.] // Vestnik YUzhno-Ural'skogo gosudarstvennogo universiteta. Seriya: Energetika – 2020. – Т. 20, № 4. – С. 103-111. – DOI 10.14529/power200412.


Review

For citations:


Frolov M.Yu. State estimation of synchronous generator mode parameters based on Kalman filter. Power engineering: research, equipment, technology. 2025;27(4):98-107. (In Russ.) https://doi.org/10.30724/1998-9903-2025-27-4-94-103

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ISSN 1998-9903 (Print)
ISSN 2658-5456 (Online)