A major concern for interconnected power grid systems is low frequency oscillation, which limits the scalability and transmission capacity of power systems. Un-damped, or poorly-damped oscillations will lead to undesirable conditions or even a catastrophic system blackout. Real-time synchrophasor data can be used to reliably detect and control these low frequency oscillations in order to mitigate their catastrophic impact. In this paper two low complexity tracking algorithms are proposed to identify and monitor low frequency oscillations; namely a fast subspace tracking algorithm and a gradient descent based fast recursive algorithm. Initially, both methods perform a one-time Matrix Pencil Method on the power spectrum matrix of real-time PMU data to detect low frequency oscillations. This is then followed by two different low-complexity algorithms to fast track the low frequency oscillations. While the first method uses a recursive Fast Data Projection Method (FDPM)-based algorithm, the latter runs a gradient-descent based fast recursive algorithm on every PMU to track and monitor low frequency oscillations. Both methods have been compared to other state of the art techniques, such as Matrix Pencil method, FDD and TLS-ESPRIT. We have shown that the proposed approaches are capable of achieving performance with high accuracy, especially in terms of computational complexity for a large system with many PMUs.
and Gharavi, H.
Real-Time Low-Frequency Oscillations Monitoring, Journal of Renewable and Sustainable Energy, [online], https://doi.org/10.1063/5.0051338, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932787
(Accessed December 6, 2021)