New signal subspace approach to estimate the inter‐area oscillatory modes in power system using TLS‐ESPRIT algorithm

The authors propose here a new low‐frequency oscillatory (LFO) modes estimation scheme by applying Karhunen–Loeve transform (KLT) with total least square estimation of signal parameter employing rotational invariance technique (TLS‐ESPRIT). The proposed KLT provides the perfect decorrelation of the signal from the noise at low signal‐to‐noise ratio and for both white and coloured noise. KLT also maximally compress the energy of the estimated signal. KLT helps to estimate the signal subspace perfectly for any non‐stationary ambient power signal with the fastest computational time. First, the authors applied the KLT denoise to the filtered signal buried with highly correlated coloured Gaussian noise through the first eigenvectors of the correlation matrix and estimated the signal subspace. Finally, TLS‐ESPRIT is applied to estimate the modes of the power system. A comparative analysis of the KLT‐TLS‐ESPRIT with the modified TLS‐ESPRIT, multi‐channel wavelet transform, data driven‐stochastic subspace identification, TLS‐ESPRIT, Root‐MUSIC, and improved Prony method is presented. To explore the real‐time application of the KLT‐TLS‐ESPRIT, the authors considered Kundur's two‐area test system and 16‐machine 68‐bus system using 50,000 Monte‐Carlo simulations. The obtained results show that the KLT‐TLS‐ESPRIT accurately estimates the LFO with least mean and standard deviation as compared to the remaining six methods.