AI 中文总结
研究IBR集成电力系统中动态轨迹预测问题，提出基于网络相互依存关系的ML算法。用模块化时空注意力网络建模IBR单元，提出混合物理信息损失函数，提升预测精度与鲁棒性，优于现有基于ML的轨迹预测方法。
AI 中文摘要
在存在专有电子控制架构的情况下，基于逆变器资源（IBR）的黑箱建模对于实时电网运行和控制至关重要。现有的基于机器学习（ML）的使用IBR黑箱模型的在线动态轨迹预测方法，要么在同时使用多个代理时显著累积预测误差，要么忽略测量误差，限制了它们在实际电网中的部署。为了解决这些限制，本文提出了一种新颖的基于网络相互依存关系的ML算法，用于IBR集成电力系统中的在线动态轨迹预测。首先提出了一种基于模块化时空注意力网络（STAN）的预测器，用于每个IBR单元的黑箱建模。利用过去的测量数据，所提出的STAN可以通过采用注意力机制来关注轨迹预测最相关的特征，有效地捕获和预测IBR的时空动态。此外，还提出了一种新颖的混合物理信息损失函数，该函数集成了解耦线性化交流潮流公式。所提出的损失函数有效地确保了网络运行中预测的物理一致性，同时避免了迭代潮流求解的计算复杂性，从而实现了高效的梯度反向传播和整体提高的预测精度。在IEEE 14和WECC 179节点系统上的案例研究表明，所提出的方法在测量误差方面实现了显著的精度提高和鲁棒性，优于最近基于ML的轨迹预测方法。
英文摘要
Black-box modeling of inverter-based resources (IBRs) has attracted growing interest for real-time grid operation and control in the presence of proprietary electronic control architectures. Existing machine learning (ML)-based online dynamic trajectory prediction approaches using IBR black-box models either significantly accumulate prediction errors when multiple surrogates are simultaneously used or ignore measurement errors, limiting their deployment in practical grids. To address these limitations, this paper proposes a novel network interdependency-informed ML algorithm for online dynamic trajectory prediction in IBR-integrated power systems. A modular spatiotemporal attention network (STAN)-based predictor for the black-box modeling of each IBR unit is first proposed. Utilizing past measurements, the proposed STAN can effectively capture and predict the spatiotemporal dynamics of IBRs by employing an attention mechanism to attend to the most pertinent features for trajectory prediction. Furthermore, a novel hybrid physics-informed loss function that integrates a decoupled linearized AC power flow formulation is proposed. The proposed loss function effectively ensures physical consistency of predictions within network operation while avoiding the computational complexity of iterative power flow solving, thereby enabling efficient gradient backpropagation and overall improved prediction accuracy. Case studies on the IEEE 14- and WECC 179-bus systems demonstrate that the proposed method achieves significant accuracy enhancement and robustness against measurement errors, outperforming recent ML-based trajectory prediction methods.
