Abstract
This paper investigates the performance of a fast converging adaptive filter, the Recursive Least Squares algorithm based on the Inverse QR Decomposition (IQRD-RLS), with an exact initialization procedure, for the online estimation of low-damped electromechanical modes in a power system. In this approach, the modes are tracked from ambient data, once it is assumed that load variations constantly excite the electromechanical dynamics as a nearly white noise input. Monte Carlo linear simulations are run on the full Brazilian Interconnected Power System model to generate power system ambient data. The performance of the IQRD-RLS algorithm is compared to that of the Least Mean Squares (LMS) algorithm when estimating the slowest interarea mode in the system.
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