Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/29066
Title: A feature-subspace-based ensemble method for estimating long-term voltage stability margins
Authors: Khurram, Ambreen 
Gusnanto, Arief 
Aristidou, Petros 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Online voltage stability;Bayesian optimization;Machine learning
Issue Date: Nov-2022
Source: Electric Power Systems Research, 2022, vol. 212, articl. no. 108481
Volume: 212
Journal: Electric Power Systems Research 
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.
URI: https://hdl.handle.net/20.500.14279/29066
ISSN: 18732046
DOI: 10.1016/j.epsr.2022.108481
Rights: © Elsevier
Type: Article
Affiliation : Cyprus University of Technology 
University of Leeds 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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