Xu, YijunWang, QinlingMili, Lamine M.Zheng, ZongshengGu, WeiLu, ShuaiWu, Zhi2024-01-232024-01-232023-08-150885-8950https://hdl.handle.net/10919/117625A prerequisite to dynamic state estimation of a stochastic nonlinear dynamic model of a power system is its observability analysis. However, due to the model nonlinearity, the traditional methods either suffer from a poor estimation accuracy if a linear approximation is performed or yield an extremely complicated procedure if the Lie-derivative method is applied to a large-scale power system. To address these weaknesses, we propose a new data-driven Koopman-based observability method by revealing the link that exists between the Koopman operator and the Lie-derivative in the Koopman canonical coordinates. This enables the proposed data-driven method not only to be fully <italic>derivative-free</italic>, which alleviates its implementation complexity but also overcomes the model nonlinearity and inaccuracy of the system. Furthermore, as an important byproduct, the proposed observability analysis scheme provides a valuable guide for the selection of the <italic>observables</italic> of the Koopman operator, which is a major difficulty for the application of this operator. Finally, we demonstrate the excellent performance of the proposed method on several IEEE standard test systems.Pages 1-15application/pdfenIn CopyrightArticle - Refereedhttps://doi.org/10.1109/TPWRS.2023.3305404PP99Mili, Lamine [0000-0001-6134-3945]1558-0679