Change search
CiteExportLink to record
Permanent link

Direct link
Cite
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
  • rtf
Experimental Real-Time Testing of a Decentralized PMU Data-Based Power Systems Mode Estimator
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.ORCID iD: 0000-0002-6090-1674
2017 (English)In: 2017 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents the results and testing of a Phasor Measurement Unit (PMU) data-based mode estimation application deployed within a decentralized architecture using a real-time test platform. This work is a continuation of that in [1], which described a decentralized mode estimation architecture that enables the application to better detect local modes whose observability is affected by other more observable modes. The tests in this paper were carried out using an active distribution network (ADN) comprised of a high voltage network connected to a distribution grid including renewable energy resources (RES). The developed application was run in a decentralized architecture where each PMU was associated with its own processing unit which was running the application to estimate modes from the time-series data. The results of the decentralized mode estimation architecture are analyzed and compared with its centralized counterpart.

Place, publisher, year, edition, pages
IEEE , 2017.
Series
IEEE Power and Energy Society General Meeting PESGM, ISSN 1944-9925
Keyword [en]
Power System Monitoring, Phasor Measurement Unit (PMU), Mode-meter, Decentralized Mode Estimation, Oscillations
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-228184ISI: 000426921802007ISBN: 978-1-5386-2212-4 OAI: oai:DiVA.org:kth-228184DiVA, id: diva2:1208890
Conference
2017 IEEE Power & Energy Society General Meeting, JUL 16-20, 2017, Chicago, IL
Note

QC 20180521

Available from: 2018-05-21 Created: 2018-05-21 Last updated: 2018-05-21Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

http://www.pes-gm.org/2017/

Authority records BETA

Hooshyar, HosseinVanfretti, Luigi

Search in DiVA

By author/editor
Singh, Ravi ShankarHooshyar, HosseinVanfretti, Luigi
By organisation
Electric Power and Energy SystemsKTH
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 3 hits
CiteExportLink to record
Permanent link

Direct link
Cite
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
  • rtf