Generator Parameter Calibration by Adaptive Approximate Bayesian Computation With Sequential Monte Carlo Sampler

Abstract

Secure power system operation relies on accurate steady-state and dynamic system models. It is thus crucial to carefully validate the models in power systems, in particular the generator models. The phasor measurement unit (PMU) technologies provide a low-cost option for generator model validation and parameter calibration without interfering with their operation. In this paper, an empirical parameter sensitivity Gramian based approach is developed to identify the critical parameters from a nonlinear system perspective. We further propose a synchrophasor measurement based generator parameter calibration method by adaptive Approximate Bayesian Computation with a sequential Monte Carlo sampler (A-ABC-SMC) that avoids directly dealing with likelihood functions. We propose adaptive threshold sequence scheme and perturbation kernel function in A-ABC-SMC in order to improve the computational efficiency. The effectiveness of the proposed method is validated for a hydro generator against multiple system events. The simulation results show that the proposed approach can accurately and efficiently estimate the full probabilistic posterior distributions of the generator parameters even when there are gross errors in the parameters' prior distributions.