Edge Computing for Microgrid via MATLAB Embedded Coder and Low-Cost Smart Meters

Fuente: arXiv
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Autores principales: Xu, Linna, Huang, Jian, Yang, Shan, Zhu, Yongli
Formato: Preprint
Publicado: 2024
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author Xu, Linna
Huang, Jian
Yang, Shan
Zhu, Yongli
author_facet Xu, Linna
Huang, Jian
Yang, Shan
Zhu, Yongli
contents In this paper, an edge computing-based machine-learning study is conducted for solar inverter power forecasting and droop control in a remote microgrid. The machine learning models and control algorithms are directly deployed on an edge-computing device (a smart meter-concentrator) in the microgrid rather than on a cloud server at the far-end control center, reducing the communication time the inverters need to wait. Experimental results on an ARM-based smart meter board demonstrate the feasibility and correctness of the proposed approach by comparing against the results on the desktop PC.
format Preprint
id arxiv_https___arxiv_org_abs_2412_01080
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Edge Computing for Microgrid via MATLAB Embedded Coder and Low-Cost Smart Meters
Xu, Linna
Huang, Jian
Yang, Shan
Zhu, Yongli
Systems and Control
In this paper, an edge computing-based machine-learning study is conducted for solar inverter power forecasting and droop control in a remote microgrid. The machine learning models and control algorithms are directly deployed on an edge-computing device (a smart meter-concentrator) in the microgrid rather than on a cloud server at the far-end control center, reducing the communication time the inverters need to wait. Experimental results on an ARM-based smart meter board demonstrate the feasibility and correctness of the proposed approach by comparing against the results on the desktop PC.
title Edge Computing for Microgrid via MATLAB Embedded Coder and Low-Cost Smart Meters
topic Systems and Control
url https://arxiv.org/abs/2412.01080