Identification of linear data-driven models for large-scale power systems

Abstract. The linearization of the full model of electrical power systems is of great significance for the adoption of linear analysis techniques to examine the system’s dynamic characteristics, as well as for the design and tuning of practical controllers. Typically, the state-space model of the power system is first obtained from the time domain model. Linear analysis and controller tuning are then performed utilizing the linear state-space model. This approach however often has several practical limitations, such as the unavailability of a time domain model, when only simulation or measurement data is available, or the lack of linearization capability in the software tool in which the time domain model is available. Moreover, the linearization of the time domain models of large-scale power systems results in very high-dimension state-space models, which greatly complicates further analysis. To this aim, in this paper, suitable linear data-driven models of reduced order are identified for power systems to retain the most relevant modes of oscillations of the original system. A commercial rigorous software is used for the data generation and a well-established Python toolbox is used for the model identification: different models and techniques are applied and then compared in terms of accuracy and simplicity.