Understanding power system dynamics is essential for interarea oscillation analysis and the detection of grid instabilities. The FNET/GridEye is a GPS-synchronized wide-area frequency measurement network that provides an accurate picture of the normal real-time operational condition of the power system dynamics, giving rise to new and intricate spatiotemporal patterns of power loads. We propose to model FNET/GridEye grid frequency data from the U.S. Eastern Interconnection with a spatiotemporal statistical model. We predict the frequency data at locations without observations, a critical need during disruption events where measurement data are inaccessible. Spatial information is accounted for either as neighboring measurements in the form of covariates or with a spatiotemporal correlation model captured by a latent Gaussian field. The proposed method is useful in estimating power system dynamic response from limited phasor measurements and holds promise for predicting instability that may lead to undesirable effects such as cascading outages.

中文翻译：

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使用时空建模的电网频率预测

了解电力系统动态对于区域间振荡分析和电网不稳定性检测至关重要。FNET/GridEye 是一种 GPS 同步广域频率测量网络，可提供电力系统动态正常实时运行状况的准确图像，从而产生新的和复杂的电力负载时空模式。我们建议使用时空统计模型对来自美国东部互联网络的 FNET/GridEye 电网频率数据进行建模。我们在没有观察的情况下预测频率数据，这是无法访问测量数据的中断事件期间的关键需求。空间信息可以作为协变量形式的相邻测量值或由潜在高斯场捕获的时空相关模型来解释。