Khurram, A, Gusnanto, A and Aristidou, P orcid.org/0000-0003-4429-0225 (2022) A Feature-subspace-based Ensemble Method for Estimating Long-term Voltage Stability Margins. Electric Power Systems Research, 212. 108481. ISSN 0378-7796
Abstract
This study proposes a methodology for online voltage stability monitoring using a feature subspace based ensemble approach. The overall idea is to use the input from varied feature selectors for the ensemble and aggregate their outputs. This approach is superior to conventional feature selection methods because it can handle stability issues that are usually poor in existing feature selection methods and improve performance. The selected features are used as an input to three different regression algorithms to enable online voltage stability monitoring. A Bayesian optimization technique is used to tune machine learning (ML) models’ hyper-parameters and determine the optimal number of features. The proposed approach is evaluated in experiments using simulated data from the Nordic test system. The simulation results have shown that the proposed method efficiently predicts the status of dynamic voltage stability in the test system.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier B.V. All rights reserved. This is an author produced version of an article, published in Electric Power Systems Research. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Voltage stability; Feature selection; Machine learning; Bayesian optimization; Regression methods |
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) > Institute of Communication & Power Networks (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Aug 2022 14:24 |
Last Modified: | 18 Jul 2023 00:13 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.epsr.2022.108481 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189676 |
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