Sensitivity Matrix Based Parameter Identifiability Analysis for Generator Dynamic Models

In this paper, we develop an efficient method for performing parameter identifiability analysis for the generator dynamic model by utilizing the parameter sensitivity matrix, which includes all derivatives of the model outputs with respect to the parameters. Then, we present a parameter ranking technique based on the rank-revealing QR decomposition of the right singular vectors associated with the non-zero singular values of the sensitivity matrix. This technique ensures that unidentifiable parameters are consistently positioned at the end of the reordered parameter list. To validate the effectiveness of the proposed approach, we conduct experiments on a hydro generator model. The simulation results demonstrate that the sensitivity matrix based approach can accurately and efficiently assess parameter identifiability, facilitating the identification of candidate parameters for calibration.