Modal Analysis of Spatial Load Correlation in AI Data Center-Dominated Power Systems

Abstract

Hyperscale AI data centers induce spatially and temporally correlated load fluctuations that violate classical independence assumptions and are not captured by time-averaged spectral methods. These correlations are episodic and non-stationary, requiring analysis that resolves transient structure. This paper applies Dynamic Mode Decomposition (DMD) to the temporal evolution of pairwise inter-bus correlation coefficients to form a low-dimensional state representation that enables modal analysis without a stationarity assumption. DMD eigenvalues encode the correlation regime: their location in the complex plane distinguishes sustained coherence, decaying transients, and intensifying events, while oscillation frequency maps to underlying physical coupling mechanisms. Using an IEEE 39-bus Real-Time Digital Simulator (RTDS) testbed with three converter-interfaced AI data center loads driven by synthetic workload profiles, global DMD provides a time-averaged modal baseline in a slow thermal band ($f \approx 0.005$\,Hz, $|μ| = 0.91$) captures 93.6\% of total correlation energy. A sliding-window DMD formulation identifies transient intensification events: 51 of 775 windows (6.6\%) satisfy the $|μ_k^{(n)}| > 1$ criterion, which aligns with stochastic workload coincidences. Cross-validation with RTDS voltage coherence confirms elevated coupling during these intervals. The proposed modal growth indicator provides an early-warning signal of correlation intensification prior to peak pairwise coherence.