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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10006950 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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