Excitation System Parameters Estimation by Different Input Signals and Various Kalman Filters

Mohseni, Adel; Doroudi, Aref; Karrari, Mehdi

doi:10.30503/eecs.2023.406895.1042

Document Type : Original Article

Authors

¹ EE Department, Faculty of Engineering, Shahed University, Tehran, Iran

² Associate professor in Shahed university

³ EE Department, Amirkabir University of Technology, Tehran, Iran

https://doi.org/10.30503/eecs.2023.406895.1042

Abstract

The excitation system is one of the most crucial components of a power plant. Knowing the exact parameters of the excitation system and their changes over time is essential for efficient and accurate power system dynamic studies. In this paper, the parameters of a typical and well-known type of excitation system are estimated using different types of Kalman filters, including unscented Kalman filter (UKF), spherical-simplex Kalman filter (SS-UKF), and cubature Kalman filter (CKF). The efficacy of these Kalman filter methods in the excitation system parameters estimation problem is investigated under three different planned and unplanned events as the input of the methods. The planned disturbances will be internal type (a reference voltage step) and external type (unit transformer tap changing) whereas the unplanned disturbance is caused by power grid events (neighbor generator outage). Comparison is done between the simulation results and experimental ones and the best appropriate approach is selected.

Keywords

Main Subjects

Control of Electromechanical systems

References

[1] Kundur P (1994) Power System Stability and Control. New York, NY: McGraw-Hill.

[2] Huang Z, Kosterev DN, Guttromson R, Nguyen T (2006) Model validation with hybrid dynamic simulation. In: IEEE Power Engineering Society General Meeting; Montreal, Que., Canada.

[3] Mohseni A, Doroudi A (2013) Key Parameter Identification of Power Plant Using GA. In: PSC2013; Tehran, Iran.

[4] Aghamohamadi MR, Beik A, Rezaii M (2009) The effect of the inaccuracy of synchronous generator parameters on transient stability performance of generators and the power system. In: PSC2009; Tehran, Iran.

[5] Kou G, Markham P, Hadly S, King T, Liu Y (2016) Impact of Governor Deadband on Frequency Response of the U.S. Eastern Interconnection. IEEE Transactions on Smart Grid; 7(3):1368-1377

[6] Karrari M (2017) System Identification. Tehran,Iran: AmirKabir University.

[7] Zaker B, Gharehpetian GB, Karrari M, Moaddabi N (2016) Simultaneous Parameter Identification of Synchronous Generator and Excitation System Using Online Measurements. IEEE Transaction On Smart Grid; 7(3):1230-1238.

[8] Qin X, Lin H, Yu D, Zhou Sh (2018) Parameter Identification of Nonlinear Excitation System Based on Improved Adaptive Genetic Algorithm. In: 10th Asia-Pacific Power and Energy Engineering Conference (APPEEC 2018); Guilin, China.

[9] Zha WH, Yuan Y, Zhang T (2011) Excitation Parameter Identification Based on the Adaptive Inertia Weight Particle Swarm Optimi. Springer Advanced Electrical and Electronics Engineering LN in EE 2011; 87:369-374.

[10] Canakoglu A, Yetgin A, Temurtas H, Turan M (2014) Induction motor parameter estimation using metaheuristic methods. Turk J Elec Eng and Comp Sci; 22:1177-1192.

[11] Aghamolki HG, Miao Z, Fan L, Jiang W, Manjure D (2015) Identification of synchronous generator model with frequency controlusing unscented Kalman filter. Elsevier, Electric Power Systems Research; 126:45-55.

[12] Xie Z, Feng J (2012) Real-time ninlinear Structtral System Identification Via Iterated Unscented Kalman Filter. Elsevier, Mechanical System and Signal Processing; 28:309-322.

[13] Qi J, Taha AF, Wang J (2018) Comparing Kalman Filters and Observers for Power System Dynamic State Estimation with Model Uncertainty and Malicious Cyber Attacks. IEEE Access; 6: 77155-77168.

[14] Pourbeik P, Rhinier R, Hsu S, Agrawal B, Bisbee R (2013) Semiautomated Model Validation of Power Plant Equipment Using Online Measurements. IEEE Transaction On Energy Conversion, June; 28(2):308-316.

[15] Bhaskar R, Crow M, Ludwig E, Erickson K, Shah K (2000) Nonlinear parameter estimation of excitation systems. IEEE Transactions on Power Systems; 15(4):1225-1231.

[16] Pourbeik P, Pink C, Bisbee R (2011) Power plant model validation for achieving reliability standard requirements based on recorded on-line disturbancedata. In: IEEE Power Syst. Conf. Expo; Phoenix,AZ, USA.

[17] Hajnoroozi A, Aminifar F, Ayoubzadeh H (2015) Generating Unit Model Validation and Calibration Through Synchrophasor Measurements. IEEE Transaction on Smart Grid; 6(1):441-449.

[18] Huang Z, Guttromson R, Hauer JF (2004) Large-scale hybrid dynamic simulation employing field measurements. In: IEEE Power Engineering Society General Meeting; Denver, CO, USA.

[19] Huang Z, Nguyen T, Kosterev DN, Guttro R (2006) Model Validation of Power System Components Using Hybrid Dynamic Simulation. In: IEEE PES Transmission and Distribution Conference and Exhibition; Dallas, TX, USA.

[20] Chowdlhary G, Jategaonkar R (2010) Aerodynamic Parameter Estimation from Flight Data Applying Extended and Unsented Kalman Filter. Elsevier AeroSpace Science and Technology; 14:106-117.

[21] Duan J, Shi H, Liu D, Yu H (2016) Square Root Cubature Kalman Filter-Kalman Filter Algorithm for Intelligent Vehicle Position Estimate. Elsevier Procedia Engineering; 137:267 - 276.

[22] Dubey , Chakrabarti S (2016) An Unscented Kalman Filter Based Hybrid State Estimator Considering Conventional and PMU Measurements. In: IEEE 6th International Conference on Power Systems; New Delhi, India.

[23] Julier SJ (2003) The spherical simplex unscented transformation. In: American Control Conference; Denver, CO, USA.

[24] Hong-de D, Shao-wu D, Yuan-cai C, Guang-bin W (2012) Performancerea Comparison of EKF/UKF/CKF for the Tracking of Ballistic Target. ELKOMNIKA; 10(7):1537-1542.

[25] Arasaratnam I, Haykin S, Hurd TR (2010) Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations. IEEE Transaction on Signal Processing; 58(10):4977-4993.

[26] I. S. 421.5 Std. (2005) IEEE Recommended Practice for Excitation System Models for Power System Stability Studies, IEEE Power Engineering Society

Electromechanical Energy Conversion Systems

Excitation System Parameters Estimation by Different Input Signals and Various Kalman Filters

References

References

Volume 2, Issue 2
April 2022
Pages 40-51

Excitation System Parameters Estimation by Different Input Signals and Various Kalman Filters

References

References

Volume 2, Issue 2April 2022Pages 40-51

Volume 2, Issue 2
April 2022
Pages 40-51