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@ARTICLE{Ferdowsi:866112,
author = {Ferdowsi, Mohsen and Benigni, Andrea and Monti, Antonello
and Ponci, Ferdinanda},
title = {{M}easurement {S}election for {D}ata-{D}riven {M}onitoring
of {D}istribution {S}ystems},
journal = {IEEE systems journal},
volume = {13},
number = {4},
issn = {1932-8184},
address = {New York, NY},
publisher = {IEEE},
reportid = {FZJ-2019-05331},
pages = {4260 - 4268},
year = {2019},
abstract = {This article investigates the problem of measurement
selection for data-driven monitoring approaches. Several
approaches to input variable selection (IVS) are analyzed,
and a general procedure for finding the optimal order for
the selection of candidate measurements is presented. The
method is based on the extensions of partial correlation and
minimal redundancy maximum relevance criteria to support IVS
problems involving multiple outputs. This method can be used
to find the minimal set of measurements for achieving a
target estimation accuracy. The results demonstrate the
advantages and limits of the introduced method in comparison
to the other approaches discussed in this article.},
cin = {IEK-10},
ddc = {004},
cid = {I:(DE-Juel1)IEK-10-20170217},
pnm = {899 - ohne Topic (POF3-899)},
pid = {G:(DE-HGF)POF3-899},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000503182300064},
doi = {10.1109/JSYST.2019.2939500},
url = {https://juser.fz-juelich.de/record/866112},
}