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Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks

2024Artigo científico Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorShafei, Atefeh Pour
dc.contributor.authorSilva, J. Fernando A.
dc.contributor.authorMonteiro, J.
dc.contributor.editorElsevier
dc.date.accessioned2026-09-24T10:52:47Z
dc.date.available2026-09-24T10:52:47Z
dc.date.issued2024
dc.description.abstractPower outages significantly impact the power industry by disrupting social welfare and economic stability. Still, existing methods for fault detection face challenges due to load and network topology, conditions, and installed equipment. However, recent advances in artificial intelligence (AI) are enabling researchers to create alternative approaches for fault detection and location strategies. Therefore, this paper introduces a novel method for detecting, classifying, and locating faults in power systems through voltage waveform analysis using a convolutional neural network (CNN) integrated with the Piecewise Function Put Together (PFPT) algorithm for fault detection and fault zone localization in a power distribution network. Utilizing Park's transformation, noise reduction PFPT sine fitting, and CNNs, the proposed method distinguishes between 'healthy' and 'faulty' conditions. Simulation results reveal that while the voltage Park's vector time behavior of a healthy system remains stable, it exhibits circular or mixed patterns under faulty conditions. These patterns enable the identification of four types of short circuit faults—single-line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), and three-line (3L) faults—by analyzing 3D voltage Park's waveforms at network buses. The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. By converting 3D voltage waveforms into high-resolution images, the method utilizes a CNN for fault recognition, achieving an accuracy of 93.1%. This innovative approach underscores the robustness and precision of combining traditional electrical engineering techniques with modern AI.eng
dc.identifier.citationShafei, A. P., Silva, J. F. A., & Monteiro, J. (2024). Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks. e-Prime - Advances in Electrical Engineering, Electronics and Energy, 10, Article 100820. https://doi.org/10.1016/j.prime.2024.100820
dc.identifier.doi10.1016/j.prime.2024.100820
dc.identifier.eissn2772-6711
dc.identifier.urihttp://hdl.handle.net/10400.21/23191
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S2772671124004005?via%3Dihub
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectFault detection and characterization
dc.subjectDistribution network
dc.subjectPark transform
dc.subjectMachine learning
dc.subjectConvolutional neural network (CNN)
dc.titleConvolutional neural network approach for fault detection and characterization in medium voltage distribution networkspor
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDB/50021/2020
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT
oaire.citation.issue100820
oaire.citation.titlee-Prime - Advances in Electrical Engineering, Electronics and Energy
oaire.citation.volume10
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isProjectOfPublication1f3e7ad3-87bb-4203-919b-53592c18fcea
relation.isProjectOfPublication.latestForDiscovery1f3e7ad3-87bb-4203-919b-53592c18fcea

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