LAUSR

Accurate model parameters of Static Var Generator (SVG) play an essential role in regulating bus voltage profiles of power grid with increased penetration of renewable energy under various contingencies. Aiming at addressing the known issues of low identification accuracy and long computation time faced by the traditional SVG parameter identification methods, this paper presents a multi-layer coarse-to-fine grid searching approach for calibrating SVG dynamic model parameters using particle swarm optimization. First, actual measurement data is collected through SVG-RTDS testbeds under various conditions, which is compared with transient stability simulation results to check for model accuracy. Then, nonlinear trajectory sensitivity analysis is performed using segmented curves to identify potential bad model parameters. Next, a multi-layer coarse-to-fine grid searching mechanism is used to narrow the parameter searching space, before particle swarm algorithm optimization is used for more precise identification of parameters. By comparing the identification results obtained by the traditional identification methods and the proposed approach via comprehensive case studies, it is found that the proposed coarse-to-fine parameter identification method achieved higher accuracy and faster computational speed.