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Concern for climate change is driving a vastly increased use of electricity and variable renewable energy supply encourages larger and evermore interconnected power systems. Stability analysis and short-term prediction of power system output has never been more urgent or more complex. Many distributed and renewable generators contribute zero inertia to the system and increase the risk of poorly damped oscillations leading to cascading outage. Data-driven techniques, higher order dynamic mode decomposition (HODMD) and total-least-squares higher-order dynamic mode decomposition (THDMD) are applied to modal analysis and short-term prediction of frequency and power exchange deviations. The decomposition uses multiple and randomized sampling windows of historical measurements. Dominant THDMD and HODMD modes can be used to show the contribution of renewable generation, such as wind power, to wide-area oscillations. The developed techniques are applied to the analysis of blackouts in Europe (2006) and the UK (2019), as well as the separation event in Australia (2018). The obtained results demonstrate that the damping of some HODMD modes can be overestimated. Although selected HODMD modes can reconstruct and predict power system output, the results are not always reliable. In turn, THDMD can predict dominant oscillations, with reduction of noise bias error in modal analysis of noisy measurements. With low noise data both techniques can produce very similar modal results. • We consider application of data-driven models to prediction of power systems. • We identify dominant modes to carry out stability analysis. • We apply noise reduction techniques. • We consider applications to well-known blackouts in Europe (2006) and the UK (2019) as well as the separation event in Australia (2018). [ABSTRACT FROM AUTHOR]

Academic discussion continues as to whether a fleet of grid-connected wind farms, widely dispersed across a single grid network, can provide a reliable electricity supply. One opinion is that wide geographical dispersion of wind farms provides sufficient smoothing of the intermittent and highly variable output of individual wind farms enabling the wind farm fleet to provide for base load demand. In an examination of the 5-minute time-averaged wind farm operational data for 21 large wind farms connected to the eastern Australian grid - geographically the largest, most widely dispersed, single interconnected grid in the world (AER, [1]) - this paper challenges that opinion. The findings also suggest that the connection of such a wind farm fleet, even one that is widely dispersed, poses significant security and reliability concerns to the eastern Australian grid. These findings have similar implications for the impact of wind farms on the security of electricity grids worldwide. [ABSTRACT FROM AUTHOR]