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Most influential feature form for supervised learning in voltage sag source localization
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.ORCID iD: 0000-0001-8660-5569
Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, 2000, Slovenia.
Universidade Federal do Rio Grande do Sul, Osvaldo Aranha, 99, RS, Porto Alegre, 90035-190, Brazil.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.ORCID iD: 0000-0003-2960-3094
2024 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 133, no Part D, article id 108331Article in journal (Refereed) Published
Abstract [en]

The paper investigates the application of machine learning (ML) for voltage sag source localization (VSSL) in electrical power systems. To overcome feature-selection challenges for traditional ML methods and provide more meaningful sequential features for deep learning methods, the paper proposes three time-sample-based feature forms, and evaluates an existing feature form. The effectiveness of these feature forms is assessed using k-means clustering with k = 2 referred to as downstream and upstream classes, according to the direction of voltage sag origins. Through extensive voltage sag simulations, including noises in a regional electrical power network, k-means identifies a sequence involving the multiplication of positive-sequence current magnitude with the sine of its angle as the most prominent feature form. The study develops further traditional ML methods such as decision trees (DT), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), an ensemble learning (EL), and a designed one-dimensional convolutional neural network (1D-CNN). The results found that the combination of 1D-CNN or SVM with the most prominent feature achieved the highest accuracies of 99.37% and 99.13%, respectively, with acceptable/fast prediction times, enhancing VSSL. The exceptional performance of the CNN was also approved by field measurements in a real power network. However, selecting the best ML methods for deployment requires a trade-off between accuracy and real-time implementation requirements. The research findings benefit network operators, large factory owners, and renewable energy park producers. They enable preventive maintenance, reduce equipment downtime/damage in industry and electrical power systems, mitigate financial losses, and facilitate the assignment of power-quality penalties to responsible parties.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 133, no Part D, article id 108331
Keywords [en]
Voltage sag (dip), Source localization, Supervised and unsupervised learning, Convolutional neural network, Time-sample-based features
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:umu:diva-223198DOI: 10.1016/j.engappai.2024.108331Scopus ID: 2-s2.0-85189522853OAI: oai:DiVA.org:umu-223198DiVA, id: diva2:1850779
Funder
The Kempe Foundations, JCK22-0025Available from: 2024-04-11 Created: 2024-04-11 Last updated: 2024-04-15Bibliographically approved

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Mohammadi, YounesKhodadad, Davood

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CiteExportLink to record
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