TY - JOUR
T1 - Application of Robust PCA with a structured outlier matrix to topology estimation in power grids
AU - Chrétien, Stéphane
AU - Clarkson, Paul
AU - Garcia, Maria Segovia
PY - 2018/9/30
Y1 - 2018/9/30
N2 - 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.
AB - 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.
KW - non-convex optimization
KW - Robust PCA
KW - tree structured sparsity
UR - http://www.scopus.com/inward/record.url?scp=85043506774&partnerID=8YFLogxK
UR - https://www.sciencedirect.com/journal/international-journal-of-electrical-power-and-energy-systems
U2 - 10.1016/j.ijepes.2018.02.003
DO - 10.1016/j.ijepes.2018.02.003
M3 - Article
AN - SCOPUS:85043506774
SN - 0142-0615
VL - 100
SP - 559
EP - 564
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
ER -