  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
\underline{a}uto\underline{r}egression with e\underline{x}ogenous 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.
