Identification of nonlinear dynamic load models using an alternating least squares approach

Load modeling has a significant impact on power systems dynamic simulation and control. In this paper, an alternating least squares approach is proposed to decompose the nonlinear optimization problem of dynamic load parameter identification to two linear least squares problems, which can be solved in an alternating manner. A DFT-based numerical derivative technique is also proposed to attenuate the effect of noises in the process of parameter estimation. Case studies are conducted using simulation data and laboratory measurements to demonstrate the effectiveness of the proposed methods.