2015/03/17 by Bander Mogharbel, Mogharbel, Bander, Lingling Fan +3
Computer Science · Engineering · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Electrical engineering #Model Reduction and Neural Networks #Power System Optimization and Stability #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1503.05224
openalex publication_date 2015/03/17 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
In this paper, least square estimation (LSE)-based dynamic generator model parameter identification is investigated. Electromechanical dynamics related parameters such as inertia constant and primary frequency control droop for a synchronous generator are estimated using Phasor Measurement Unit (PMU) data obtained at the generator terminal bus. The key idea of applying LSE for dynamic parameter estimation is to have a discrete \underlineauto\underlineregression with e\underlinexogenous input (ARX) model. With an ARX model, a linear estimation problem can be formulated and the parameters of the ARX model can be found. This paper gives the detailed derivation of converting a generator model with primary frequency control into an ARX model. The generator parameters will be recovered from the estimated ARX model parameters afterwards. Two types of conversion methods are presented: zero-order hold (ZOH) method and Tustin method. Numerical results are presented to illustrate the proposed LSE application in dynamic system parameter identification using PMU data.