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Support vector machine for classification of voltage disturbances
University College of Borås.
Chalmers University of Technology.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0003-4074-9529
2007 (English)In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 22, no 3, p. 1297-1303Article in journal (Refereed) Published
Abstract [en]

The support vector machine (SVM) is a powerful method for statistical classification of data used in a number of different applications. However, the usefulness of the method in a commercial available system is very much dependent on whether the SVM classifier can be pretrained from a factory since it is not realistic that the SVM classifier must be trained by the customers themselves before it can be used. This paper proposes a novel SVM classification system for voltage disturbances. The performance of the proposed SVM classifier is investigated when the voltage disturbance data used for training and testing originated from different sources. The data used in the experiments were obtained from both real disturbances recorded in two different power networks and from synthetic data. The experimental results shown high accuracy in classification with training data from one power network and unseen testing data from another. High accuracy was also achieved when the SVM classifier was trained on data from a real power network and test data originated from synthetic data. A lower accuracy resulted when the SVM classifier was trained on synthetic data and test data originated from the power network.

Place, publisher, year, edition, pages
2007. Vol. 22, no 3, p. 1297-1303
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Energy Engineering
Research subject
Electric Power Engineering; Energy Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-2612DOI: 10.1109/TPWRD.2007.900065ISI: 000247605900002Scopus ID: 2-s2.0-38949086030Local ID: 03f3dd10-a498-11dc-8fee-000ea68e967bOAI: oai:DiVA.org:ltu-2612DiVA, id: diva2:975465
Note
Validerad; 2007; 20071207 (matbol)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Bollen, Math

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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