Application of Robust PCA with a structured outlier matrix to topology estimation in power grids

Stéphane Chrétien, Paul Clarkson, Maria Segovia Garcia

Research output: Contribution to journalArticle

1 Citation (Scopus)
13 Downloads (Pure)

Abstract

Robust PCA is a widely used technique for Principal Component Analysis when the data is corrupted by outliers. The goal of the present short note is to report on the performance results of a simple modification of the method of Netrapali et al. for estimating Low Rank + Sparse models where the sparse matrix has the structure of a tree. We demonstrate the efficiency of the approach on the problem of estimating the topology in power grid networks.

Original languageEnglish
Pages (from-to)559-564
Number of pages6
JournalInternational Journal of Electrical Power and Energy Systems
Volume100
Early online date20 Mar 2018
DOIs
Publication statusPublished - 30 Sep 2018

Keywords

  • non-convex optimization
  • Robust PCA
  • tree structured sparsity

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