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A Study on the Sensitivity Matrix in Power System State Estimation by Using Sparse Principal Component Analysis
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2016 (English)In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, 1529-1535 p., 7798483Conference paper, Published paper (Refereed)
Abstract [en]

This paper analyzes the joint impact of uncertainties in the input data on the power system state estimator. The approach is based on the sensitivity analysis of the estimated telemetry data with respect to the measurement data and the branch parameters with the main goal of locating relevant input components. In order to find relevant inputs, we analyze the normalized sensitivity matrix by sparse principal component analysis (PCA). The non-zero entries of the loading vectors related to the dominant principal components are considered to be the relevant inputs to the state estimator as they mainly contribute to the amplification of the estimated values. It turns out that PCA shows an elementary structure of the sensitivity matrix: All non-zero entries of a loading vector corresponding to a positive singular value belong either to the telemetry data or to the branch data. We show that this property is also valid for PCA with different sparsity-promoting constraints on the loading vector. The proposed analysis method is demonstrated by a numerical study.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. 1529-1535 p., 7798483
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-208623DOI: 10.1109/CDC.2016.7798483ISI: 000400048101114Scopus ID: 2-s2.0-85010806105ISBN: 978-1-5090-1837-6 (print)OAI: oai:DiVA.org:kth-208623DiVA: diva2:1107481
Conference
55th IEEE Conference on Decision and Control, CDC 2016, ARIA Resort and Casino, Las Vegas, United States, 12 December 2016 through 14 December 2016
Funder
Swedish Energy AgencySweGRIDS - Swedish Centre for Smart Grids and Energy Storage
Note

QC 20170609

Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2017-06-09Bibliographically approved

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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
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