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Additional information from voltage dips
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0003-4074-9529
Number of Authors: 2
2016 (English)In: 17th International Conference on Harmonics and Quality of Power, Piscataway, NJ, 2016, 326-332 p., 7783434Conference paper, (Refereed)
Abstract [en]

This paper presents some methods to extract additional information from voltage dip recordings, beyond residual voltage and duration. Additionally it discusses some issues related to the massive amount of data obtained from modern measurements that, is referred to as Big Data. The paper proposes some Deep Learning based algorithms as good candidates to extract complex features from big data as a step towards additional information. The applications of the information include predicting individual equipment performance, fault type and location, protection operation, and overall load behavior. Individual equipment and overall load include production as well as consumption

Place, publisher, year, edition, pages
Piscataway, NJ, 2016. 326-332 p., 7783434
Series
International Conference on Harmonics and Quality of Power, ISSN 1540-6008
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-61646DOI: 10.1109/ICHQP.2016.7783434ISI: 000391424200058Scopus ID: 2-s2.0-85009508247ISBN: 9781509037926 (electronic)OAI: oai:DiVA.org:ltu-61646DiVA: diva2:1068747
Conference
17th International Conference on Harmonics and Quality of Power (ICHQP), Belo Horizonte, Brasil, 16-19 October 2016
Available from: 2017-01-26 Created: 2017-01-26 Last updated: 2017-03-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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