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Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference

A multiple input multiple output (MIMO) power line communication (PLC) model for industrial facilities was developed that uses the physics of a bottom-up model but can be calibrated like top-down models. The PLC model considers 4-conductor cables (three-phase conductors and a ground conductor) and h...

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Autores principales: Wonnacott, Raelynn, Ching, David S., Chilleri, John, Safta, Cosmin, Rashkin, Lee, Reichardt, Thomas A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006950/
https://www.ncbi.nlm.nih.gov/pubmed/36904620
http://dx.doi.org/10.3390/s23052416
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author Wonnacott, Raelynn
Ching, David S.
Chilleri, John
Safta, Cosmin
Rashkin, Lee
Reichardt, Thomas A.
author_facet Wonnacott, Raelynn
Ching, David S.
Chilleri, John
Safta, Cosmin
Rashkin, Lee
Reichardt, Thomas A.
author_sort Wonnacott, Raelynn
collection PubMed
description A multiple input multiple output (MIMO) power line communication (PLC) model for industrial facilities was developed that uses the physics of a bottom-up model but can be calibrated like top-down models. The PLC model considers 4-conductor cables (three-phase conductors and a ground conductor) and has several load types, including motor loads. The model is calibrated to data using mean field variational inference with a sensitivity analysis to reduce the parameter space. The results show that the inference method can accurately identify many of the model parameters, and the model is accurate even when the network is modified.
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spelling pubmed-100069502023-03-12 Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference Wonnacott, Raelynn Ching, David S. Chilleri, John Safta, Cosmin Rashkin, Lee Reichardt, Thomas A. Sensors (Basel) Article A multiple input multiple output (MIMO) power line communication (PLC) model for industrial facilities was developed that uses the physics of a bottom-up model but can be calibrated like top-down models. The PLC model considers 4-conductor cables (three-phase conductors and a ground conductor) and has several load types, including motor loads. The model is calibrated to data using mean field variational inference with a sensitivity analysis to reduce the parameter space. The results show that the inference method can accurately identify many of the model parameters, and the model is accurate even when the network is modified. MDPI 2023-02-22 /pmc/articles/PMC10006950/ /pubmed/36904620 http://dx.doi.org/10.3390/s23052416 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wonnacott, Raelynn
Ching, David S.
Chilleri, John
Safta, Cosmin
Rashkin, Lee
Reichardt, Thomas A.
Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference
title Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference
title_full Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference
title_fullStr Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference
title_full_unstemmed Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference
title_short Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference
title_sort industrial plc network modeling and parameter identification using sensitivity analysis and mean field variational inference
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006950/
https://www.ncbi.nlm.nih.gov/pubmed/36904620
http://dx.doi.org/10.3390/s23052416
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