Overview of Data-Driven Methods for Wideband Oscillation Identification and Early-Warning in Power Systems

With the advancement of sustainable development goals, traditional power systems are evolving into the Double-High systems, characterized by high renewable energy integration and extensive use of power electronics. This transformation has introduced challenges related to wideband oscillations, which affect grid stability and renewable energy integration. This paper explores data-driven methods for identifying and forecasting wideband oscillations, addressing key challenges and future research opportunities. It first outlines the features of these oscillations, including their wideband characteristics, spatiotemporal distribution, and strong time-variation and nonlinearity, and emphasizes the benefits of data-driven approaches for identification and early-warning. The paper then reviews the practical applications and challenges of these methods in oscillation detection and source localization. Finally, it discusses future prospects, focusing on data acquisition, algorithm generalization, and interpretability.