Data-Driven Transient Stability Assessment by Modal Patterns-Based Severity Indices

This paper proposes a methodology for performing data-driven transient stability assessment by exploiting the modal patterns extracted from multiple synchrophasor measurements. The strategy behind this methodology is powered by a state-of-the-art technique for extracting modal patterns which is named the Fourier-Cosine filter bank. This technique processes multivariate signals by decomposing all their components and providing different features such as amplitude estimates, which in turn, feed three stability indices that are assembled into a general performance index for assessing and ranking the system stability when large disturbances take place and different levels of renewable energy sources are integrated. The performance and effectiveness of the proposal are corroborated and validated in a simulation environment that employs the benchmark 68-bus and 16-machine system equipped with a wind penetration of 5%, 10%, 15%, and 20%. Finally, it is demonstrated that the inclusion of modal trajectories improves the severity indices' quality,