Graph Neural Networks for Voltage Stability Margins With Topology Flexibilities
Graph Neural Networks for Voltage Stability Margins With Topology Flexibilities
复制标题
DOI：
10.1109/oajpe.2022.3223962
复制
发表时间：
2023-07
影响因子：
3.8
通讯作者：
K. Guddanti;Yang Weng;Antoine Marot;Benjamin Donnot;P. Panciatici
中科院分区：
文献类型：
--
作者：
K. Guddanti;Yang Weng;Antoine Marot;Benjamin Donnot;P. Panciatici
分布式能源（DER）的高渗透率改变了电网中的流量，导致热故障，这是由实时校正拓扑切换管理。电压稳定裕度是电网拓扑结构调整的重要约束条件。然而，它是不平凡的穷举搜索使用AC功率流（ACPF）的所有控制动作与所需的VSM。灵敏度方法被用来解决这个问题的“潮流的自由VSM估计”筛选候选控制动作。然而，由于DER的易失性，灵敏度方法不能很好地执行附近的非线性工作区域，这是克服通过求解ACPF。在这里，我们提出了一种新的VSM估计方法，在非线性工作区域，而无需解决ACPF。我们通过制定图神经网络的学习，如无矩阵潮流算法来实现这一点。我们凭经验证明了这种相似性如何绕过不准确的问题，并在看不见的操作条件和拓扑结构上表现良好，而无需进一步重新训练。的有效性被证明在一个电力网络与现实的负载和发电配置文件，各种发电组合，和大的控制行动。这些好处体现在速度、识别不安全控制的可靠性以及对未知场景和电网拓扑的适应性方面。
High penetration of distributed energy resources (DERs) changes the flows in power grids causing thermal congestions which are managed by real-time corrective topology switching. It is crucial to consider voltage stability margin (VSM) as a constraint when modifying grid topology. However, it is nontrivial to exhaustively search using AC power flow (ACPF) for all control actions with desired VSM. Sensitivity methods are used to solve this issue of “power flow-free VSM estimation” to screen candidate control actions. However, due to the volatile nature of DERs, sensitivity methods do not perform well near nonlinear operating regions which is overcome by solving ACPF. Here, we propose a new VSM estimation method that performs well at nonlinear operating regions without solving ACPF. We achieve this by formulating the learning of graph neural networks like the matrix-free power flow algorithms. We empirically demonstrate how this similarity bypasses the inaccuracy issues and performs well on unseen operating conditions and topologies without further re-training. The effectiveness is demonstrated on a power network with realistic load and generation profiles, various generation mix, and large control actions. The benefits are showcased in terms of speed, reliability to identify insecure controls, and adaptability to unseen scenarios and grid topologies.