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工业 PLC 网络建模与参数识别:基于灵敏度分析与平均场变分推断。

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

机构信息

Sandia National Laboratories, 7011 East Avenue, Livermore, CA 94550, USA.

出版信息

Sensors (Basel). 2023 Feb 22;23(5):2416. doi: 10.3390/s23052416.

DOI:10.3390/s23052416
PMID:36904620
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10006950/
Abstract

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.

摘要

为工业设施开发了一种多输入多输出 (MIMO) 电力线通信 (PLC) 模型,该模型使用自下而上模型的物理原理,但可以像自上而下模型一样进行校准。该 PLC 模型考虑了 4 芯电缆(三相导体和一根接地导体),并具有多种负载类型,包括电机负载。该模型使用均值场变分推断进行校准,并进行了敏感性分析以减小参数空间。结果表明,推断方法可以准确识别模型的许多参数,并且即使网络发生变化,模型也很准确。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/52f5143b8769/sensors-23-02416-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/eeb520ccb102/sensors-23-02416-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/93382b16b62e/sensors-23-02416-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/df9f3cc0a2ac/sensors-23-02416-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/4493bff5e82d/sensors-23-02416-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/f004df2f393c/sensors-23-02416-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/709467cd053b/sensors-23-02416-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/10dfc1a84b45/sensors-23-02416-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/fd60106ed48e/sensors-23-02416-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/7995bd74b434/sensors-23-02416-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/4e3a69b415c6/sensors-23-02416-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/599713c1edf9/sensors-23-02416-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/c5e431c37ea0/sensors-23-02416-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/52f5143b8769/sensors-23-02416-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/eeb520ccb102/sensors-23-02416-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/93382b16b62e/sensors-23-02416-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/df9f3cc0a2ac/sensors-23-02416-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/4493bff5e82d/sensors-23-02416-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/f004df2f393c/sensors-23-02416-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/709467cd053b/sensors-23-02416-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/10dfc1a84b45/sensors-23-02416-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/fd60106ed48e/sensors-23-02416-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/7995bd74b434/sensors-23-02416-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/4e3a69b415c6/sensors-23-02416-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/599713c1edf9/sensors-23-02416-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/c5e431c37ea0/sensors-23-02416-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a6b/10006950/52f5143b8769/sensors-23-02416-g012.jpg

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