The problem of power system oscillation stability has become more and more prominent in the context of a high proportion of new energy sources and the gradual increase in power electronic devices. Broadband oscillations pose new challenges to the security and stability of power systems. In recent years, the frequency of power system oscillations around the world, especially those triggered by wind, solar, and power electronic devices such as flexible direct current (DC) transmission, has shown that the geographic and system scale of their impacts continue to expand. Failure to properly control these broadband oscillations can lead to serious consequences such as equipment damage, off-grid renewable energy generation systems, and large-scale blackouts. Two strategies, data-driven and model-driven, have been used for monitoring and controlling broadband oscillations, but each has its limitations. The data-driven approach relies on data quality, while the model-driven approach requires high accuracy of the system model. For this reason, hybrid data-model-driven strategies have emerged. They combine the advantages of both to improve the accuracy and robustness of system analysis. In this paper, we will discuss the principle of hybrid data-model driving and its application to broadband oscillations, classify different frequency oscillations, and introduce risk warning methods, and finally summarize future research challenges such as quantitative analysis, propagation mechanisms and suppression measures of broadband oscillations.
Fan et al. (Wed,) studied this question.