A Dynamic Mode Decomposition Framework for Global Power System Oscillation Analysis

TLDR

The technique is well suited for fast wide‑area monitoring and assessment of global instability in modern data‑fusion estimation contexts. The study proposes a global multiscale DMD method to characterize transient processes recorded by wide‑area sensors. The method interprets global dynamics through spatial and temporal patterns of dynamic modes, extracting mode shapes, frequencies, growth and decay rates, and uses them to detect coherent dominant structures. Application to large, high‑dimensional data sets yielded encouraging results.

Abstract

A global multiscale method based on a dynamic mode decomposition (DMD) algorithm to characterize the global behavior of transient processes recorded using wide-area sensors is proposed. The method interprets global dynamic behavior in terms of both, spatial patterns or shapes and temporal patterns associated with dynamic modes containing essentially single-frequency components, from which the mode shapes, frequencies and growth and decay rates of the modes can be extracted simultaneously. These modes are then used to detect the coherent and dominant structures within the data. The technique is well suited for fast wide-area monitoring and assessment of global instability in the context of modern data fusion-based estimation techniques. Results of the application of the proposed method to large, high-dimensional data sets are encouraging.