发表机构
Pacific Northwest National Laboratory（太平洋西北国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种分布式边缘到云架构，利用状态机驱动流水线在负荷显著变化时触发FCM重辨识，通过树莓派边缘处理并仅传输JSON更新，现场验证中减少99.6%数据传输且保持谐波精度。
AI 中文摘要
配电网络边缘电力电子负荷的快速普及，使得准确的谐波负荷建模对于电能质量评估、变压器降容和电网规划变得至关重要。现有的频率耦合矩阵（FCM）辨识方法生成静态的一次性模型，随着负荷构成的变化，这些模型会迅速失去保真度。本文提出了一种分布式边缘到云架构，用于在实际现场条件下自主、连续地重新辨识FCM，其贡献包括两个方面：（i）一个由状态机驱动的流水线，用于监测逐点波形（PoW）测量数据，并仅在负荷发生统计显著变化时触发FCM重新辨识；（ii）一种边缘到服务器的架构，将波形处理限制在低成本的树莓派节点上，仅在确认变化后传输紧凑的JSON模型更新，而非流式传输原始PoW数据。通过住宅现场部署验证，该框架在31个监测周期中的3个周期内正确触发了重新训练，同时将谐波精度保持在80%阈值以上，与连续流式传输相比，上行数据传输量减少了99.6%。
英文摘要
The rapid proliferation of power electronic loads at the distribution grid edge has made accurate harmonic load modeling critical for power quality assessment, transformer derating, and grid planning. Existing Frequency Coupling Matrix (FCM) identification methods produce static, one-time models that rapidly lose fidelity as load composition evolves. This paper proposes a distributed edge-to-cloud architecture for autonomous, continual FCM re-identification under real field conditions, with two contributions: (i) a state-machine-driven pipeline that monitors point-on-wave (PoW) measurements and triggers FCM re-identification only upon statistically significant load change; and (ii) an edge-to-server architecture confining waveform processing to a low-cost Raspberry Pi node, transmitting only compact JSON model updates upon confirmed change rather than streaming raw PoW data. Validated through a residential field deployment, the framework correctly triggered retraining in 3 of 31 monitoring cycles while maintaining harmonic accuracy above the 80% threshold, reducing upstream data transmission by 99.6% relative to continuous streaming.
Comments5 pages
