Embedded Machine Learning for Solar PV Power Regulation in a Remote Microgrid
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912140407865344 |
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| author | Zhu, Yongli Xu, Linna Huang, Jian |
| author_facet | Zhu, Yongli Xu, Linna Huang, Jian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01054 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Embedded Machine Learning for Solar PV Power Regulation in a Remote Microgrid Zhu, Yongli Xu, Linna Huang, Jian Systems and Control Machine Learning 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. |
| title | Embedded Machine Learning for Solar PV Power Regulation in a Remote Microgrid |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2412.01054 |