Dynamic Model Validation for Large-Scale Networks Using the Dominant Time Intervals

A large number of power system equipment and their various operating conditions could make the system-wide model validation process a rigorous and time-consuming task. This paper presents a new approach for parameter identification in large-scale power system networks by estimating each group of the parameters through a different event during the dominant intervals of the process time. This technique provides a faster convergence in order to achieve the lowest mismatch between the simulation and measured disturbance responses. The proposed method is implemented on the 18-machine 118-bus system model. Governor droop, ramp rate, and dead band parameters are estimated using the Particle Swarm Optimization algorithm (PSO) coupled with the genetic mutation process. The accuracy of the estimated parameters is investigated through two different system disturbances.