Real-Time Edge-based Detection of Correlated AI Data-Center Load Episodes
Chandan Chaudhary Abanish Tiwari Yansong Pei Mohammed Ben-Idris Joydeep Mitra
作者信息
Abstract
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.引用本文复制引用
Chandan Chaudhary,Abanish Tiwari,Yansong Pei,Mohammed Ben-Idris,Joydeep Mitra.Real-Time Edge-based Detection of Correlated AI Data-Center Load Episodes[EB/OL].(2026-08-24)[2026-09-01].https://arxiv.org/abs/2608.22719.学科分类
电工基础理论/电气测量技术、电气测量仪器/自动化技术、自动化技术设备
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