Sparse learning of network-reduced models for locating low frequency oscillations in power systems. (15th March 2020)

• Record Type: Journal Article
• Authors: Ping, Zuowei; Li, Xiuting; He, Wei; Yang, Tao; Yuan, Ye
• Is Part Of: Applied energy. Volume 262(2020)
• Publication Date: 2020-03-15
• Subjects: Low frequency oscillation -- Network-reduced model -- Kron reduction -- Power systems -- Swing equations -- Sparse Bayesian learning; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.114541
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Damping mechanism of power system stability is fundamentally studied. It designs the criterion to locate sources of low frequency oscillations efficiently. A data-driven method is proposed to characterize low frequency oscillation. It can estimate damping coefficients and topological parameters simultaneously. Abstract: With increasing penetration intermittent renewable energy and the interactions between different power sources in an interconnected system, low frequency oscillations may occur and potentially threaten the security of power systems if the grid cannot support adequate damping. Locating sources of low frequency oscillations is of great importance, which needs finding the mechanism of the damping of low frequency oscillations for interpretation. The difficulty of this problem lies in the fact that the parameters associated with the power system model can range from slightly uncertain to entirely unknown. Therefore, we firstly focus on identifying the network-reduced model to characterize low frequency oscillation, and then utilizing Hamilton analysis to reveal mechanism of oscillation. Accordingly, the problem of locating low frequency oscillation is equivalent to identify equivalent negative damping coefficient. In this paper, we propose a novel data-driven method that estimates equivalent damping coefficients and topological parameters of the network-reduced model simultaneously. More specifically, the proposed method utilizes the sparse representation to select the most dominant nonlinear terms from a set of dictionary functions, which finally balances the data fitness and achieves dynamics learning. We validate and evaluate the proposed method on IEEE 9-bus test system and IEEE 39-bus test system. The results demonstrate the effectiveness of the proposed method in achieving dynamics learning and locating sources of low frequency oscillations from measurement data.