Assessing Risks of Hydro-Generator Shaft Fatigue from Data Center Load Oscillations
For power system engineers and hydro plant operators, it provides a practical metric (Goodman safety factor) to evaluate and mitigate fatigue risks from data center-induced oscillations, but the work is incremental as it applies existing fatigue and torsional analysis methods to a specific load scenario.
This paper develops a model-based framework to assess hydro-generator shaft fatigue risk from persistent sub-synchronous oscillations caused by AI data center loads, finding that lower inertia ratios and reduced damping increase fatigue exposure, with Kaplan-type units more susceptible than Francis or Pelton units.
Large AI data center loads can introduce persistent sub-synchronous active-power oscillations that may impact nearby generators by exciting torsional modes and increasing shaft stress. This paper presents a model-based framework for evaluating hydro-generator shaft fatigue risk under oscillatory loading. An electromagnetic transient simulation model is developed using a two-mass turbine-generator shaft representation with parameters from real-world generation units and a configurable AI data center load. The risk assessment is performed in two stages. First, a network transfer function quantifies the propagation of load oscillations from the data center point of interconnection to the hydro-generator terminal. A plant transfer function then characterizes the resulting shaft torque amplification. A frequency-scan approach identifies resonance regions and evaluates torque amplification at individual forcing frequencies. Parametric studies show that amplification is strongly affected by generator-to-turbine inertia ratio and torsional damping. Lower inertia ratios shift torsional modes to lower frequencies and increase amplification, indicating that some Kaplan-type units may be more susceptible than comparable Francis or Pelton units. Reduced damping further increases resonant response and fatigue exposure. A simplified fatigue assessment based on S--N curves and the Goodman diagram relates simulated torque response to mechanical integrity. The resulting Goodman safety factor provides a practical metric for evaluating the impact of persistent AI data center oscillations on hydro-generator service life and supports interconnection studies, oscillation limits, and plant-level monitoring strategies.