Embedded Machine Learning for Solar PV Power Regulation in a Remote Microgrid

2024/12/02 by Zhu, Yongli, Xu, Linna, Huang, Jian · 1 citation
#FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2412.01054

Abstract

This paper presents a machine-learning study for solar inverter power regulation in a remote microgrid. Machine learning models for active and reactive power control are respectively trained using an ensemble learning method. Then, unlike conventional schemes that make inferences on a central server in the far-end control center, the proposed scheme deploys the trained models on an embedded edge-computing device near the inverter to reduce the communication delay. Experiments on a real embedded device achieve matched results as on the desktop PC, with about 0.1ms time cost for each inference input.

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• On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence

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