Khurram, A, Gusnanto, A and Aristidou, P (2021) Detection of Oscillatory Modes in Power Systems using Empirical Wavelet Transform. In: 2021 IEEE Madrid PowerTech. 2021 IEEE Madrid PowerTech, 28 Jun - 02 Jul 2021, Madrid, Spain. IEEE ISBN 978-1-6654-1173-8
Abstract
In electric power systems, detecting inter-area oscillations is crucial to the system operators for maintaining the security of the grid – especially in the case of unstable oscillatory behaviour. However, extracting information from unstable, noisy, signals is complicated with conventional signal processing tools suffering from insufficient adaptability. In this paper, we propose a method based on Empirical Wavelet Transform (EWT) to estimate in real-time the dominant inter-area modes in electricity grids. EWT extracts the inherent modulation information by decomposing the signal into its mono components under an orthogonal basis. The instantaneous amplitude and instantaneous frequency is estimated by applying Hilbert transform from the narrow band components of the decomposed EWT signal. The performance of the proposed method is demonstrated using the Nordic test system.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Oscillatory instability; inter-area oscillations; Empirical Wavelet Transform; Hilbert Transform; PMU |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Jun 2021 13:14 |
Last Modified: | 17 Oct 2023 14:28 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/PowerTech46648.2021.9494761 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:175244 |