发表机构
Pacific Northwest National Laboratory（太平洋西北国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电网仿真模型与实际动态差异问题，提出学习增广混合方法，用AI残差模型补充物理模型，并构建持续学习自适应框架，在IEEE 68节点基准上验证了残差学习与自适应能力。
AI 中文摘要
现代电力系统可能面临运行人员仿真模型与电网实际真实动态之间差异增大的问题，这些差异由多种不确定性因素驱动，例如新型逆变器资源（IBRs）的集成、大型负荷、未建模动态、参数漂移等。所有这些因素都会影响控制室运行，在暂态研究中可能无法捕获某些关键振荡。为规避这些问题，我们提出了一种学习增广的混合方法，其中运行人员仿真模型通过人工智能（AI）学习的残差模型进行补充，该残差模型利用基于相量测量单元（PMU）/点对波（PoW）的感知轨迹数据。基于物理的运行模型提供了可解释性和结构一致性，而学习到的残差则捕获由非理想因素引起的差异。所学习的模型采用先进的神经架构，包含一个骨干编码器和多头解码器层，用于处理异构电网通道。随后，我们构建了一个受持续学习启发的自适应框架，使得当底层真实电网模型在未来条件下发生变化时，基线残差AI模型也能得到更新。我们在IEEE 68节点基准模型上进行了大量数值仿真，考虑了多种扰动，并探索了涉及循环学习器、潜在神经ODE和变换器的不同最先进预测架构，以展示残差学习和自适应能力。
英文摘要
Modern power systems can encounter increased discrepancy between the operators' simulation model and the actual true dynamics of the grid, driven by uncertainties caused by integration of new inverter-based resources (IBRs), large loads, unmodeled dynamics, parameter drifts, etc., to name a few. All of these impact the control room operations, where some critical oscillations may not be captured during the transient studies. To circumvent these issues, we propose a learning-augmented hybrid approach where the operator simulation model is supplemented with artificial intelligence (AI)-learned residual models using the phasor measurement unit (PMU)/ point-on-wave (PoW) based sensed trajectory data. The physics-based operator model provides interpretability and structural consistency, while the learned residual captures discrepancies caused by non-idealities. The learned model employs advanced neural architectures and consists of a backbone encoder and multi-head decoder layers for heterogeneous grid channels. Subsequently, we formulated a continual learning-motivated adaptation framework such that the baseline residual AI model can also be updated when the underlying real grid model changes in future conditions. Extensive numerical simulations are performed on the IEEE 68-bus benchmark model with a diverse set of disturbances, and different state-of-the-art predictive architectures involving recurrent learners, latent neural ODEs, and transformers are explored to demonstrate both residual learning and adaptation capabilities.
Comments12 pages, 5 figures, 3 tables
