Proceedings of the 7th International Conference on Management, Education, Information and Control (MEICI 2017)

The PLC Signals' Noise Mitigating Algorithm with PCA

Authors
MingYue Zhai
Corresponding Author
MingYue Zhai
Available Online October 2017.
DOI
10.2991/meici-17.2017.71How to use a DOI?
Keywords
Smart grids; PLC; PCA; Noise mitigation
Abstract

Power Line Communication (PLC) systems suffer from too severe noise contamination in the channels. In the paper, a new scheme was proposed based on the principal components analyses(PCA) to mitigate noises in PLC channels. The scheme consists of four steps. In the first step, the PLC signal with one dimension is generalized to a data set with multiple dimensions (degrees). In order to de-correlate the new data set, generalized signals are whitened with Gaussian noises in the second step. For the uncorrelated data set, the PCA technology is applied to cancel noises in the transformed domain. As the last step, the PLC signals of interesting are recovered by applying the inverse transform matrix. The results from the simulated seismic signals and the measurements data verify the validity of the proposed scheme.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 7th International Conference on Management, Education, Information and Control (MEICI 2017)
Series
Advances in Intelligent Systems Research
Publication Date
October 2017
ISBN
10.2991/meici-17.2017.71
ISSN
1951-6851
DOI
10.2991/meici-17.2017.71How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - MingYue Zhai
PY  - 2017/10
DA  - 2017/10
TI  - The PLC Signals' Noise Mitigating Algorithm with PCA
BT  - Proceedings of the 7th International Conference on Management, Education, Information and Control (MEICI 2017)
PB  - Atlantis Press
SP  - 374
EP  - 379
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-17.2017.71
DO  - 10.2991/meici-17.2017.71
ID  - Zhai2017/10
ER  -