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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