使用SCADA遥测检测同步AI数据中心负荷事件
Detection of Synchronized AI Data Center Load Episodes Using SCADA Telemetry
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中文总结 AI 辅助
本文提出一种基于SCADA遥测的同步指数与CUSUM检验方法，无需新增设备即可检测AI数据中心负荷同步事件，并在IEEE 39节点系统上验证了其有效性与归因能力。
中文摘要 AI 辅助
运行分布式训练工作负载的AI数据中心会对输电电网造成间歇性的、空间相关的有功功率扰动。这些同步事件增加了跨变电站的负荷相关性，并限制了备用容量规划所假设的多样化收益。传统能源管理系统独立评估每个变电站，无法提取定义同步事件的跨变电站统计结构。本文开发了一种检测方法，能够从标准有功功率遥测数据中识别同步的AI数据中心负荷事件，无需新增仪器、训练分类器或标记数据。该方法计算同步指数，即跨变电站滑动样本协方差矩阵的主特征值占比。累积和（CUSUM）序贯检验将该指数转换为具有延迟界限的事件警报。相同的特征分解在无额外成本的情况下，产生一个主特征向量，将检测到的事件归因于驱动它的变电站。在包含三个AI数据中心母线的IEEE 39节点系统的实时数字仿真器（RTDS）模型上的测试表明，该方法以远超偶然性的宽裕度区分事件窗口和正常窗口，并在变电站粒度上归因事件参与。
英文摘要
AI data centers running distributed training workloads impose episodic, spatially correlated active-power disturbances on the transmission grid. These synchronized episodes increase cross-substation load correlation and limit the diversification benefit that reserve margin planning assumes. Conventional energy management systems evaluate each substation independently and do not extract the cross-substation statistical structure that defines a synchronized episode. This paper develops a detection method that identifies synchronized AI data-center load episodes from standard active-power telemetry without new instrumentation, trained classifiers, or labeled data. The method computes a Synchronization Index, the dominant eigenvalue fraction of a sliding sample covariance matrix across substations. A cumulative-sum (CUSUM) sequential test converts the index into a delay-bounded episode alarm. The same eigen decomposition yields, at no additional cost, a dominant eigenvector that attributes a detected episode to the substations that drive it. Tests on a real-time digital simulator (RTDS) model of the IEEE 39-bus system with three AI data-center buses show that the method separates episode and normal windows with a wide margin over chance and attributes episode participation at substation granularity.
