Application of noise-filtering techniques to data-driven analysis of electric power systems based on higher-order dynamic mode decomposition

A transition to renewable energy is increasing the long-distance export of power, with reduced spinning inertia and small stability margins. In this work, we apply higher-order variants of a data-driven technique, the dynamic mode decomposition (DMD), for stability analysis and short-term prediction of power systems with renewable sources of energy. In the present paper we study the sampling duration for extraction of physically relevant dynamics suitable for prediction using noisy historical measurements of power system disturbance events. Trends in behaviour of the dominant mode are studied to estimate the singular value threshold and the delay embedding for prediction. In a sampling window around ten times the dominant periodic interval, the higher-order Kalman filtering DMD and extended Kalman filtering DMD (filtering for system identification) are newly applied to predict an historical resonance and a possible near-resonance events of single dominant frequency. In turn, multiple frequencies of a wide-area oscillatory disturbance with low-level non-Gaussian noise are best captured and predicted using the total-least-squares higher-order DMD.