Data‐driven power system linear model identification for selective modal analysis by frequency interpolations

Abstract

Abstract This paper proposes a new approach to identify a data‐based power system linear model by means of frequency interpolations, aiming to obtain a suitable system representation for selective‐modal analysis purposes. The key idea behind the identification process is the Loewner‐based frequency interpolation carried out by the Loewner matrices. The proposed approach demonstrates that the Loewner‐based frequency interpolation is able to fit a linear model that can be used for small‐signal analysis studies, since it provides the state‐space representation, the frequency response, and modal information (frequency, damping, and mode‐shape). Then, a selective modal analysis is accomplished over two test cases (Kundur and New England–New York power grids) by employing the identified linear model provided by the Loewner‐based frequency interpolation method. The attained results confirm the outstanding performance of the proposal which is validated against the small‐signal analysis and compared with the eigensystem realisation algorithm, overcoming the absolute error of the model identified with the traditional eigensystem realisation algorithm approach by at least 37 times, and properly capturing the modal information in a frequency band of concern.

Keywords

• Interpolation and function approximation (numerical analysis)
• Linear algebra (numerical analysis)
• Power systems