Conference Papers
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Item EMD based Detrending of Non-linear and Non-stationary Power System Signals(Institute of Electrical and Electronics Engineers Inc., 2021) Aalam, M.K.; Shubhanga, K.N.In electromechanical modal analysis of power systems using Wide Area Measurement System (WAMS) based setup, signal processing is complex as the signals are non-stationary and non-linear in nature. In order to get accurate modal parameters, as a first step, it is required to remove the non-linear trend of the signal. In the literature, many conventional methods such as filtering, averaging and peak detection techniques are employed for removing trend. In this paper, Empirical Mode Decomposition (EMD) method, an iterative algorithm is presented to detrend a signal. The EMD method and its variant are compared with another popularly used peak detection method referred to as the Zhou's detrending algorithm to find the efficacy of the EMD methods. To test the algorithms, a four machine, two-area power system with three-wind farms is modeled and simulated to generate the power system signals which bring out non-linear and non-stationary nature. Further, the modal characterization is carried out employing Prony analysis. © 2021 IEEE.Item Performance Analysis of VMD to Decompose, Detrend and Denoise Power System Signals(Institute of Electrical and Electronics Engineers Inc., 2024) Rathod, N.S.; Shubhanga, K.N.Variational Mode Decomposition (VMD) has gained significant attention as an effective tool for signal processing, particularly in the fields of biomedical and speech processing. This paper explores the application of VMD to decompose complex power system signals which are non-stationary and nonlinear. Standard Empirical Mode Decomposition (EMD) and its variants often encounter challenges like mode mixing, boundary problems, and parameter dependency on noise levels, which may adversely affect the accuracy and reliability of the decomposition results. Since VMD effectively addresses these challenges by providing a more robust framework for decomposition, the resulting Intrinsic Mode Functions (IMFs) have been successfully used for mode estimation, detrending and denoising of power system signals. While denoising, to automate the process of identifying noisy IMFs reliably, Noise Identification Indices (NIIs) have been used. This study employs datasets from 3-machine, 9-bus power system and real-world ISO New England (ISO-NE) power system signals to demonstrate the efficacy and applicability of VMD in practical scenarios. These findings show up the potential application of VMD for analysing power system signals to advance signal processing techniques across various fields. © 2024 IEEE.