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

Yongli Zhu; Linna Xu; Jian Huang

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arXiv · 2412.01054

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

Yongli Zhu·Linna Xu·Jian Huang

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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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BibTeX RIS

Yongli Zhu, Linna Xu, Jian Huang. 2024-12-02. Embedded Machine Learning for Solar PV Power Regulation in a Remote Microgrid. https://arxiv.org/abs/2412.01054

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