This paper presents a novel centralized, variational data assimilation approach for calibrating transient dynamic models in electrical power systems, focusing on load model parameters. With the increasing importance of inverter-based resources, assessing power systems' dynamic performance under disturbances has become challenging, necessitating robust model calibration methods. The proposed approach expands on previous Bayesian frameworks by establishing a posterior distribution of parameters using an approximation around the maximum a posteriori value. We illustrate the efficacy of our method by generating events of varying intensity, highlighting its ability to capture the systems' evolution accurately and with associated uncertainty estimates. This research improves the precision of dynamic performance assessments in modern power systems, with potential applications in managing uncertainties and optimizing system operations.

翻译：本文提出了一种新颖的集中式变分资料同化方法，用于电力系统中暂态动态模型的校准，重点关注负荷模型参数。随着基于逆变器的资源日益重要，评估电力系统在扰动下的动态性能变得更具挑战性，因此需要鲁棒的模型校准方法。该方法在现有贝叶斯框架的基础上，通过围绕最大后验值进行近似来建立参数的后验分布。我们通过生成不同强度的扰动事件来展示方法的有效性，突出其能够准确捕捉系统演化并提供相关不确定性估计的能力。本研究提高了现代电力系统动态性能评估的精度，在管理不确定性和优化系统运行方面具有潜在应用价值。