An Efficient Total Least Square Estimation of Signal Parameters Via the Rotational Invariance Technique for Modal Estimation of PMU Signal for Synchrophasor Application

Abstract

Accurate modelling of low frequency oscillations (LFOs) is essential for ensuring the stability and reliability of power systems. The total least square estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) is a popular sub-space-based mode estimation technique, which is capable of providing accurate modes even in noisy environments. However, the accuracy of the estimation of this technique decreases with increasing noise level, and it necessitates an initial idea of the number of oscillatory modes (Model order) in the signal. Therefore, the main contribution of this study is the development of an efficient Model order (MO) estimation technique that performs well in a variety of signal and noise conditions. This study presents a unified two-stage model characterisation approach for precise estimation of number of low frequency mode, in which first stage a Tunable Q-factor wavelet transform (TQWT) is used to reduce interference and enhance identification outcomes. Subsequently, a novel sequential partitioning approach is then taken into consideration in order to determine the Model Order. In order to evaluate the efficacy of the suggested method in a noisy environment, a comparative test was performed using simulation results from a recently established approach for synthetic signals, PMU data from two area four machine system, and real probing data from the Western Electricity Coordinating Council (WECC).