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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机译：本文研究了快速收敛自适应滤波器的性能，基于逆QR分解（IQRD-RLS）的递归最小二乘算法，具有精确的初始化过程，用于电力系统中的低阻尼机电模式的在线估计。在这种方法中，一旦假设负载变化不断激发 - 机电动态，就会从环境数据跟踪模式。 Monte Carlo线性模拟在全巴西互连的电力系统模型上运行，以产生电力系统环境数据。将IQRD-RLS算法的性能与最小均线（LMS）算法的性能进行比较，估计系统中最慢的交流模式。
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