Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances
AI & Future Technologies, Industrial and Digital Solutions, ÅF Pöyry AB (Afry), 411 19 Gothenburg, Sweden.
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Energivetenskap.ORCID-id: 0000-0001-5845-5620
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Energivetenskap.ORCID-id: 0000-0003-4074-9529
Department Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden.ORCID-id: 0000-0003-4759-7038
2022 (Engelska)Ingår i: Energies, E-ISSN 1996-1073, Vol. 15, nr 4, artikel-id 1283Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

This paper proposes a machine-learning-based framework for voltage quality analytics, where the space phasor model (SPM) of the three-phase voltages before, during, and after the event is applied as input data. The framework proceeds along with three main steps: (a) event extraction, (b) event characterization, and (c) additional information extraction. During the first step, it utilizes a Gaussian-based anomaly detection (GAD) technique to extract the event data from the recording. Principal component analysis (PCA) is adopted during the second step, where it is shown that the principal components correspond to the semi-minor and semi-major axis of the ellipse formed by the SPM. During the third step, these characteristics are interpreted to extract additional information about the underlying cause of the event. The performance of the framework was verified through experiments conducted on datasets containing synthetic and measured power quality events. The results show that the combination of semi-major axis, semi-minor axis, and direction of the major axis forms a sufficient base to characterize, classify, and eventually extract additional information from recorded event data.

Ort, förlag, år, upplaga, sidor
MDPI, 2022. Vol. 15, nr 4, artikel-id 1283
Nyckelord [en]
anomaly detection, machine learning, power quality, principal component analysis, space phasor model
Nationell ämneskategori
Annan elektroteknik och elektronik
Forskningsämne
Elkraftteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-89920DOI: 10.3390/en15041283ISI: 000778148300001Scopus ID: 2-s2.0-85126538910OAI: oai:DiVA.org:ltu-89920DiVA, id: diva2:1647514
Forskningsfinansiär
Energimyndigheten, P39437-1Energimyndigheten, P42979-1Trafikverket, 36267
Anmärkning

Validerad;2022;Nivå 2;2022-03-28 (hanlid);

Funder: Energiforsk

Tillgänglig från: 2022-03-28 Skapad: 2022-03-28 Senast uppdaterad: 2023-08-28Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Person

de Oliveira, Roger AlvesBollen, Math H. J.

Sök vidare i DiVA

Av författaren/redaktören
de Oliveira, Roger AlvesBollen, Math H. J.Gu, Irene Y. H.
Av organisationen
Energivetenskap
I samma tidskrift
Energies
Annan elektroteknik och elektronik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 159 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf