论文标题
使用图神经网络的强大而快速数据驱动的电力系统状态估计器
Robust and Fast Data-Driven Power System State Estimator Using Graph Neural Networks
论文作者
论文摘要
电力系统状态估计（SE）算法根据可用的测量集估算复杂的总线电压。由于相量测量单元（PMU）在传输电源系统中越来越广泛地使用，因此需要一个能够利用PMU的高样本速率的快速求解器。为此，我们提出了一种基于图神经网络（GNN）的模型训练模型的方法，以从PMU电压和当前测量结果中学习估计值，该测量结果一旦经过训练，就具有有关电源系统中的节点数量的线性计算复杂性。我们提出了对电力系统因子图的原始GNN实现，以简化电力系统总线和分支上各种类型和数量的测量。此外，我们增强了因子图，以提高GNN预测的鲁棒性。训练和测试示例是通过随机采样集的功率系统测量集生成的，并用PMU的线性SE的精确溶液注释。数值结果表明，GNN模型提供了SE溶液的准确近似值。此外，由PMU故障或使SE问题无法观察到的通信失败引起的错误具有局部效果，并且不会在电力系统的其余部分中恶化结果。
The power system state estimation (SE) algorithm estimates the complex bus voltages based on the available set of measurements. Because phasor measurement units (PMUs) are becoming more widely employed in transmission power systems, a fast SE solver capable of exploiting PMUs' high sample rates is required. To accomplish this, we present a method for training a model based on graph neural networks (GNNs) to learn estimates from PMU voltage and current measurements, which, once it is trained, has a linear computational complexity with respect to the number of nodes in the power system. We propose an original GNN implementation over the power system's factor graph to simplify the incorporation of various types and numbers of measurements both on power system buses and branches. Furthermore, we augment the factor graph to improve the robustness of GNN predictions. Training and test examples were generated by randomly sampling sets of power system measurements and annotated with the exact solutions of linear SE with PMUs. The numerical results demonstrate that the GNN model provides an accurate approximation of the SE solutions. Additionally, errors caused by PMU malfunctions or the communication failures that make the SE problem unobservable have a local effect and do not deteriorate the results in the rest of the power system.
