Robust Kalman Filter-based Method for Excitation System Parameters Identification using Real Measured Data

Document Type : Original Article


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

2 Faculty of Electrical Engineering, Amirkabir University of Technology, Tehran,Iran


The excitation system is one of the most important components of a power plant. The network operator awareness of excitation system parameters is vital for accurate and efficient power system dynamic studies and optimal retuning. In this paper, the use of KFs for estimating generator excitation system parameters is proposed. The CKF is reformulated and developed for this purpose. A step disturbance in the reference voltage of the UNITROL 6800 excitation system -manufactured by ABB- is used to confirm the proposed method. The simulations are established based on a real case in Iran's grid, and the results are compared with the metaheuristics such as GA and PSO which have been widely employed in literature since now. The case studies indicate that the proposed method is more accurate and robust than the optimization algorithms, not only from mean value point of view but also from statistical point of view. Moreover, it is much more helpful to identify the parameters whose actual values are needed for optimal retuning.


[1] P. Kundur, Power System Stability and Control, New York, NY: McGraw-Hill, 1994.
[2] Z. Huang, D. N. Kosterev, R. Guttromson and T. Nguyen, "Model validation with hybrid dynamic simulation," in IEEE Power Engineering Society General Meeting, Montreal, Que., Canada, 2006.
[3] A. Mohseni and A. Doroudi, "Key parameter identification of power plant using GA," in Power System Conference, Tehran, Iran, 2013.
[4] M. R. Aghamohamadi, A. Beik and M. Rezaii, "The effect of the inaccuracy of synchronous generator parameters on transient stability performance of generators and the power system," in Power System Conference, Tehran, Iran, 2009.
[5] N. Amjad and M. H. Velayati, "Evaluation of voltage stability dynamic loading margin considering the effect of load models, excitation system parameters and reactive power generation limits of generators with a combination method," Modeling in Engineering, vol. 8, no. 20, pp.39-55, 2010.
[7] M. Karrari, System Identification, Tehran: AmirKabir University, 2017.
[8] L. Hajagos, J. Barton, R. Berube, M. Coultes, J. Feltes and et al., "Guidelines for generator stability model validation testing," in IEEE Power Engineering Society General Meeting, Tampa, FL, USA, 2007.
[9] J. Puma and D. G. Colome, "Parameters identification of excitation system models using Genetic algorithms," IET Generation, Transmission and Distribution, vol. 2, no. 3, pp. 456-467, 2008.
[10] X. Qin, H. Lin, D. Yu, and S. Zhou, "Parameter identification of nonlinear excitation system based on improved adaptive Genetic algorithm," in: 10th Asia-Pacific Power and Energy Engineering Conference, pp. 897-902, 2018.
[11] B. Zaker, G. B. Gharehpetian, M. Karrari, and N. Moaddabi, "Simultaneous parameter identification of synchronous generator and excitation system using online measurements," IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1230-1238, 2016.
[12] A. Khodadadi, M. Nakhaee Pishkesh, B. Zaker, and M. Karrari, "Parameters identification and dynamical modeling of excitation system and generator in a steam power plant," in: 6th International Conference on Control Engineering & Information Technology, Istanbul, Turkey, 2018.
[13] W.-h. Zha, Y. Yuan and T. Zhang, "Excitation parameter identification based on the adaptive inertia weight particle swarm optimization," Springer Advanced Electrical and Electronics Engineering, vol. 87, pp. 369-374, 2011.
[14] F. Li, J. Zhang, "Dynamic modeling method for generator’s excitation system based on smart component technique," Applied Mechanics and Materials, vols. 513-517, pp. 2989-2994, 2014.
[16] Z. Huang, P. Du, D. Kosterev and S. Yang, "Generator dynamic model validation and parameter calibration using phasor measurements at the point of connection," IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1939-1949, 2013.
[17] R. Bhaskar, M. Crow, E. Ludwig, K. Erickson, and K. Shah, "Nonlinear parameter estimation of excitation systems," IEEE Transactions on Power Systems, vol. 15, no. 4, pp. 1225-1231, 2000.
[18] P. Pourbeik, C. Pink, and R. Bisbee, "Power plant model validation for achieving reliability standard requirements based on recorded on-line disturbance data," in: IEEE Power System Conference, Expo, Phoenix, AZ, USA, 2011.
[19] A. Hajnoroozi, F. Aminifar, and H. Ayoubzadeh, "Generating unit model validation and calibration through synchrophasor measurements," IEEE Transaction on Smart Grid, vol. 6, no. 1, pp. 441-449, 2015.
[20] Z. Huang, R. Guttromson, and J. F. Hauer, "Large-scale hybrid dynamic simulation employing field measurements," in: IEEE Power Engineering Society General Meeting, Denver, CO, USA, 2004.
[21] Z. Huang, T. Nguyen, D. N. Kosterev, and R. Guttro, "Model validation of power system components using hybrid dynamic simulation," in: IEEE PES, Transmission and Distribution Conference and Exhibition, Dallas, TX, USA, 2006.
[22] P. Pourbeik, R. Rhinier, S. Hsu, B. Agrawal, and R. Bisbee, "Semiautomated model validation of power plant equipment using online measurements," IEEE Transactions on Energy Conversion, vol. 28, no. 2, pp. 308-316, 2013.
[27] J. Duan, H. Shi, D. Liu, and H. Yu, "Square root Cubature Kalman filter-Kalman filter algorithm for intelligent vehicle position estimate," Elsevier Procedia Engineering, vol. 137, pp. 267-276, 2016.
[28] I. S. 421.5-2005, IEEE Recommended Practice for Excitation System Models for Power System Stability Studies, IEEE Power Engineering Society.
[29] M. Sedghi, A. Ahmadian, and M. Aliakbar-Golkar, "Assessment of optimization algorithms capability in distribution network planning: Review, comparison and modification techniques," Renewable and Sustainable Energy Reviews, vol. 66, pp. 415-434, 2016.
[30] E. K Burke and G. Kendall, Search Methodologies: "Introductory Tutorials in Optimization and Decision Support Techniques", New York, NY: Springer, 2005.