基于边缘设备的关联AI数据中心负荷事件实时检测
Real-Time Edge-based Detection of Correlated AI Data-Center Load Episodes
AI总结：
本文提出一种基于成对功率关联的检测方法，可在商用边缘硬件上实时检测多AI数据中心负荷是否空间关联，其准确率随观测窗口长度提升，能在一个观测窗口内跟踪电网状态变化。
AI中文摘要：
运行大规模同步训练的人工智能数据中心会产生亚秒级的功率波动。当多个设施同步其训练周期时，这些负荷变化会在空间上相关联，进而放大对电网的总干扰。无法获取数据中心遥测数据的电网运营商必须仅通过电气测量来推断这种关联，但所需的观测时间以及在变电站可部署硬件上进行检测的可行性尚未明确。本文开发了一种基于关联的检测方法，用于从跨设施功率测量中对多设施运行状态进行分类。分析推导和实验验证表明，检测置信度随观测窗口长度的增加而提高，其速率由负荷关联时间决定。该方法在实时硬件在环测试平台中得到验证：将经过验证的半马尔可夫数据中心负荷模型生成的负荷设定点应用于实时数字模拟器上的电磁暂态电网仿真，基于成对功率关联构建的紧凑分类器在该测试平台的边缘设备中运行，以确定数据中心的负荷变化是独立的还是空间关联的。跨设施关联在独立实现中区分了独立和关联两种情况，保留的检测准确率随观测窗口的增加而提高，与预测关系一致。原始波形网络无法泛化，支持成对关联作为判别信号。该检测器在商用边缘硬件上实时运行，针对运行模拟器的闭环演示可在一个观测窗口内跟踪状态变化。
英文摘要：
Artificial-intelligence data centers running bulk-synchronous training can impose sub-second power swings. When several facilities synchronize their training cycles, these load variations become spatially correlated and amplify the aggregate disturbance on the grid. A grid operator without access to data-center telemetry must infer this correlation from electrical measurements alone. However, the required observation time and the feasibility of detection on substation-deployable hardware remain uncharacterized. This paper develops a correlation-based detection method to classify the multi-facility operating regime from cross-facility power measurements. Analytical derivations and experimental validation show that the resulting detection confidence increases with the observation-window length at a rate governed by the load correlation time. The method is demonstrated in a real-time hardware-in-the-loop testbed, where load setpoints generated from a validated semi-Markov data-center load model are applied to an electromagnetic-transient grid simulation on a Real-Time Digital Simulator. A compact classifier built on pairwise power correlations runs on an edge device in this loop and determines whether the data-center load variations are independent or spatially correlated. The cross-facility correlation separates the independent and correlated cases across independent realizations. The held-out detection accuracy improves with the observation window, consistent with the predicted relation. A raw-waveform network fails to generalize, supporting pairwise correlation as the discriminative signal. The detector executes in real time on commodity edge hardware. A closed-loop demonstration against the running simulator tracks a regime change within one observation window.
