用嵌入式机器学习实现光伏逆变器快速功率调控
Embedded Machine Learning for Solar PV Power Regulation in a Remote Microgrid
- 在本地边缘设备部署集成学习模型，替代远程中心计算
- 单次推理耗时约0.1毫秒，实测性能与桌面端一致
- 适合对响应速度要求高的偏远微电网场景
本文研究了在偏远微电网中基于机器学习的光伏逆变器功率调节方法。针对有功和无功功率控制，分别采用集成学习方法训练模型。与传统依赖远端控制中心服务器进行推理的方案不同，本方法将训练好的模型部署于逆变器附近的嵌入式边缘计算设备上，以降低通信延迟。在真实嵌入式设备上的实验表明，其推理结果与桌面电脑上匹配，每次输入的推理耗时约为0.1毫秒。
原文摘要 · Abstract (English)
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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