A Novel Parameter Identification Approach via Hybrid Learning for Aggregate Load Modeling

Abstract

Parameter identification is the key technology in measurement-based load modeling. A hybrid learning algorithm is proposed to identify parameters for the aggregate load model (ZIP augmented with induction motor). The hybrid learning algorithm combines the genetic algorithm (GA) and the nonlinear Levenberg-Marquardt (L-M) algorithm. It takes advantages of the global search ability of GA and the local search ability of L-M algorithm, which is a more powerful search technique. The proposed algorithm is tested for load parameter identifications using both simulation data and field measurement data. Numerical results illustrate that the hybrid learning algorithm can improve the accuracy and reduce the computation time for load model parameter identifications.