Nassaj, A. orcid.org/0009-0002-0997-6272, Jegarluei, M.R., Jahromi, A.A. et al. (2 more authors) (2023) Enhanced Linear State Estimation for Power Systems Using Purely SCADA Measurements. In: Proceedings of the 2023 IEEE Belgrade PowerTech. 2023 IEEE Belgrade PowerTech, 25-29 Jun 2023, Belgrade, Serbia. Institute of Electrical and Electronics Engineers (IEEE) ISBN 978-1-6654-8778-8
Abstract
Linear state estimation (LSE) has long been sought after because of its simplicity, practicality, efficacy and computational speed. This paper presents an enhanced linear state estimation (ELSE) for power systems using purely time-non-synchronized SCADA measurements. In order to be able to derive current and voltage phasors from SCADA measurements, voltage phase angles are incorporated into the model formulation as pseudo-measurements. These pseudo-measurements can be obtained using either historical data or an analytical method based on an existing LSE. The proposed formulation can be readily solved by the weighted least squares (WLS) method. The accuracy and computational efficiency of the proposed ELSE, compared to the existing state estimation methods, are evaluated and demonstrated for several test systems.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Linear state estimation, power system operation, pseudo measurements, SCADA measurements, weighted least squares |
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) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Jul 2024 10:21 |
Last Modified: | 03 Jul 2024 10:21 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/powertech55446.2023.10202903 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214371 |