论文标题
电力系统中的状态估计利用图形神经网络
State Estimation in Electric Power Systems Leveraging Graph Neural Networks
论文作者
论文摘要
状态估计（SE）算法的目的是根据电力系统中可用的测量值将复杂的总线电压估算为状态变量。由于量音测量单元（PMU）越来越多地用于传输动力系统中，因此需要快速的SE求解器可以利用PMU的高采样速率。本文提出了培训图神经网络（GNN），以学习给定PMU电压和当前测量结果作为输入的估计值，并在评估阶段获得快速准确的预测。使用合成数据集对GNN进行了训练，该数据集是通过在功率系统中随机采样测量集创建的，并使用使用PMUS求解器的线性SE获得的解决方案标记它们。提出的结果显示了在各种测试方案中GNN预测的准确性，并解决了预测对缺失输入数据的敏感性。
The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of high sampling rates of PMUs. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.
