AI 中文总结
研究大型AI数据中心负载振荡对水轮发电机轴疲劳的风险，基于双质量涡轮发电机轴表示法开发电磁暂态仿真模型，分两阶段评估风险。通过频率扫描等方法发现放大受惯性比和阻尼影响，古德曼安全系数可评估振荡对水轮发电机寿命的影响。
AI 中文摘要
大型人工智能数据中心负载会引发持续的次同步有功功率振荡，可能通过激发扭转模式和增加轴应力影响附近的发电机。本文提出了一个基于模型的框架，用于评估振荡负载下水轮发电机轴的疲劳风险。利用具有实际发电单元参数和可配置人工智能数据中心负载的双质量涡轮发电机轴表示法，开发了一个电磁暂态仿真模型。风险评估分两个阶段进行。首先，通过网络传递函数量化负载振荡从数据中心互联点到水轮发电机端的传播。然后，通过设备传递函数表征由此产生的轴扭矩放大。频率扫描方法识别共振区域并评估各个强迫频率下的扭矩放大。参数研究表明，放大受发电机与涡轮的惯性比以及扭转阻尼的强烈影响。较低惯性比会使扭转模式移至较低频率并增加放大，这表明一些卡普兰型机组可能比可比的混流式或冲击式机组更易受影响。阻尼减小会进一步增加共振响应和疲劳暴露。基于S - N曲线和古德曼图的简化疲劳评估将模拟扭矩响应与机械完整性相关联。由此产生的古德曼安全系数为评估持续的人工智能数据中心振荡对水轮发电机使用寿命的影响提供了一个实用指标，并支持互联研究、振荡极限和电厂级监测策略。
英文摘要
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.
