This paper proposes a robust WKNN-TLS-ESPRIT algorithm that takes into account the effect of the unavailability of phasor measurement unit (PMU) data for identifying the low-frequency oscillatory modes in power systems. The main contribution of the proposed work is to create an enhanced autocorrelation matrix using a weighted K nearest neighbours (WKNN)-based predictive model to deal with such issues. In the present work, a Bayesian approach is utilized to determine the empirical number of neighbourhood parameters. The improved autocorrelation matrix is then used by total least square estimation of signal parameters via rotational invariance technique (TLS-ESPRIT) algorithm to provide a robust estimate of the modes. Robustness of the proposed method over the other methods is validated with a simulated test signal with missing data through Monte Carlo simulations at different SNRs. The effectiveness of the proposed approach is further verified on real data derived from PMU located in Western Electricity Coordinating Council grid.

本文提出了一种鲁棒的WKNN-TLS-ESPRIT算法，该算法考虑了相量测量单元（PMU）数据不可用来识别电力系统中低频振荡模式的影响。拟议工作的主要贡献是使用加权K创建了一个增强的自相关矩阵基于最近邻居（WKNN）的预测模型来处理此类问题。在当前的工作中，利用贝叶斯方法来确定经验参数的邻域参数。改进的自相关矩阵随后通过旋转不变技术（TLS-ESPRIT）算法用于信号参数的总最小二乘估计，以提供模式的鲁棒估计。通过在不同信噪比下的蒙特卡罗模拟，通过缺失数据的模拟测试信号验证了所提出方法相对于其他方法的稳健性。从位于西部电力协调委员会网格中的PMU得出的真实数据进一步验证了该方法的有效性。