Articles written in Sadhana

    • Using sparsity to estimate oscillatory mode from ambient data


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      In this paper, a sparse-technique-based representation of the signal over a learned dictionary and random decrement technique are explored to extract the oscillatory mode from the ambient data. The main contribution of the present work is to design a dictionary and compute the coefficients that best represent theclean signal to estimate the modes. In this work, the noise embedded in the ambient signal is minimized by the ambient signal in sparse domain with respect to the dictionary. Comparison between the proposed method and other methods such as nonlinear filtering, etc., has been done on the test signal, two-areapower system on the data generated through simulation in Matlab, two-area data simulated on real-time digital simulator and real measurement from Phasor data concentrator (PDC) of Indian power system and Western Electricity Coordinating Council (WECC) network.

    • A robust WKNN-TLS-ESPRIT algorithm for identification of electromechanical oscillation modes utilizing WAMS


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      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 matrixusing 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.

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