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

Abstract

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.