LAUSR

With the increasing integration of uncertain resources, e.g., renewables, electric vehicles, and demand responses, it is imperative to understand the characteristics of loads for power system analysis and control. Challenges of load modeling come from a variety of load components and time-varying compositions. In addition, the existence of outliers in measurements further complicates the problem. This paper proposes a robust time-varying parameter identification technique for composite ZIP and induction motor load models. A batch-model regression form, including time-varying parameters, is established based on state transition models and observation models with current observations and previous predictions to guarantee data redundancy. To deal with outliers, down-weighting coefficients of measurements are calculated with a projection statistics approach. Based on the batch-model regression and the down-weighting coefficients, parameter identification at each sample time is formulated as a weighted least squares optimization, which is solved by the Newton-Raphson approach with the previous estimated parameters as the initial iteration values. In addition, parameters’ sensitivities to different outliers in measurements are analyzed. Results on the IEEE 57-bus system and IEEE 118-bus system show that the proposed algorithm can robustly identify time-varying parameters for the composite load models.