用于数字孪生决策支持的数据驱动发电机暂态预测
Data-Driven Generator Transient Prediction for Digital Twin Decision Support
- University of South Carolina（南卡罗来纳大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出带控制的事件条件汉克尔动态模式分解模型，结合分裂保形校准层，开发用于发电机数字孪生决策支持的暂态预测代理模型，其计算高效且预测精度高，可提供带置信度的负荷指令评估。
AI中文摘要：
本文开发了一种经过校准的暂态预测代理模型，用于发电机数字孪生（DT）的决策支持，该模型在计划的有功和无功功率负荷指令应用前对其进行评估。所提出的带控制的事件条件汉克尔动态模式分解（Hankel-DMDc）模型，结合了延迟坐标提升、指令事件记忆特征以及事件加权汉克尔基，使得稀疏的负荷过渡动力学能够影响降阶表示和拟合动力学。该设计针对电压/频率偏差及恢复行为决定候选负荷指令是否能使系统保持在可接受范围内的区间。为了向操作员提供置信度信息，在冻结的代理模型上应用了分裂保形校准层，以形成电压和频率的事件条件联合预测带。实验结果显示，事件窗口的均方根误差为1.058 V和0.155 Hz；对于名义90%的目标，该预测带实现了90.17%的逐点联合电压-频率覆盖率，平均带宽度为3.55 V和0.566 Hz。在单个CPU核心上计算50秒开环滚动预测的时间为181 ms，约比实时快280倍，预测精度在长达5秒的时间范围内得到评估。这些结果证明了一种计算高效的发电机DT咨询框架，该框架将暂态预测与校准的不确定性相结合。
英文摘要：
This paper develops a calibrated transient forecasting surrogate model for generator digital twin (DT) decision support that evaluates planned active- and reactive power load commands before they are applied. The proposed event-conditioned Hankel Dynamic Mode Decomposition with Control (Hankel-DMDc) model combines delay-coordinate lifting, command-event memory features, and an event-weighted Hankel basis so that sparse load-transition dynamics influence the reduced representation and fitted dynamics. This design targets intervals where voltage/frequency deviations and recovery behavior determine whether a candidate load command keeps the system within acceptable limits. To provide operator-facing confidence information, a split-conformal calibration layer is applied to the frozen surrogate model to form event-conditioned joint prediction bands for voltage and frequency. The experimental results show event-window root-mean-square errors of 1.058 V and 0.155 Hz. For a nominal 90% target, the bands attain 90.17% pointwise joint voltage-frequency coverage, with mean band widths of 3.55 V and 0.566 Hz. A 50-s open-loop rollout is computed in 181 ms on a single CPU core, approximately 280 times faster than real time, with forecast accuracy evaluated over horizons up to 5 s. These results demonstrate a computationally efficient advisory framework for generator DTs that combines transient prediction with calibrated uncertainty.
