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@INPROCEEDINGS{Carta:905813,
author = {Carta, Daniele and Benigni, Andrea},
title = {{P}erformance {E}valuation of a {M}issing {D}ata {R}ecovery
{A}pproach {B}ased on {C}ompressive {S}ensing},
reportid = {FZJ-2022-01033},
pages = {1-6},
year = {2021},
abstract = {In this paper, a Compressive Sensing-based approach is
proposed to recover missing data in time series signals. The
presented technique is based on the combined application of
two mathematical techniques: the Discrete Cosine Transform
(DCT) and a $\ell_1$-minimization algorithm. The former
allows representing the system under test with reference to
a new sparse base, while the latter is one of the possible
approaches to solve a Compressive Sensing problem,
well-known for the capability of recovering undersampled
sparse signals.After presenting the state of the art and the
steps characterizing the proposed approach, the recovery
performances are tested on real voltage and current Root
Mean Square (rms) signals, stored on a database. In
particular, the different impact on the recovery of random
discontinuous values and wide missing signals is evaluated
by means of the Mean Absolute Percentage Error (MAPE).},
month = {Sep},
date = {2021-09-29},
organization = {2021 IEEE 11th International Workshop
on Applied Measurements for Power
Systems (AMPS), Cagliari (Italy), 29
Sep 2021 - 1 Oct 2021},
cin = {IEK-10},
cid = {I:(DE-Juel1)IEK-10-20170217},
pnm = {1123 - Smart Areas and Research Platforms (POF4-112) / 1122
- Design, Operation and Digitalization of the Future Energy
Grids (POF4-112)},
pid = {G:(DE-HGF)POF4-1123 / G:(DE-HGF)POF4-1122},
typ = {PUB:(DE-HGF)8},
pubmed = {WOS:000783741700030},
UT = {WOS:000783741700030},
doi = {10.1109/AMPS50177.2021.9586042},
url = {https://juser.fz-juelich.de/record/905813},
}