A two-stage framework for ambient signal based load model parameter identification

Abstract

Load modeling is a crucial part of modeling a power system. Traditional load model identification methods are mainly based on post large disturbance response, which cannot be conducted without large disturbance events. In this paper, a new approach of ambient signals based load model parameter identification is proposed to solve this problem, through which the time-varying load model parameters can be identified more frequently. First, the composite load model structure and the measurement based identification approach are introduced. Then, a two-stage identification framework to identify the load model parameters from ambient signals is proposed. In the first stage, the electromagnetic parameters are identified using an optimization method, with the predicted error as the objective function and the differential evolution as the optimization algorithm. In the second stage, the mechanical parameters are identified using linear regression. Finally, the effectiveness of the proposed identification method is validated through the simulation results in Guangdong Power Grid with practically measured ambient signals data. The proposed two-stage identification framework has shown its ability to accurately identify the load model parameters from ambient signals and the advantage over the other identification approaches.