Modal Analysis of Spatial Load Correlation in AI Data Center-Dominated Power Systems
The study developed a data-driven modal framework using Dynamic Mode Decomposition (DMD) to analyze nonstationary spatial load correlation in AI data center power systems. It extracts dominant spatial coherence modes from raw recordings, revealing how workload orchestration causes intermittent intensification events that classical methods miss, providing a diagnostic tool for network coupling.