论文标题
使用图神经网络对可持续电网进行动态稳定性分析
Towards dynamic stability analysis of sustainable power grids using graph neural networks
论文作者
论文摘要
为了减轻气候变化，需要增加可再生能源的份额。可再生能源引入了由于权力下放，减少惯性和生产波动而引起的电网挑战。可再生能量较高渗透的可持续功率网格的运行需要新的方法来分析动态稳定性。我们提供了合成功率电网动态稳定性的新数据集，并发现图形神经网络（GNN）仅在仅从拓扑信息中预测高度非线性目标方面非常有效。为了说明扩展到实数电网的潜力，我们证明了德克萨斯电网模型的成功预测。
To mitigate climate change, the share of renewable needs to be increased. Renewable energies introduce new challenges to power grids due to decentralization, reduced inertia and volatility in production. The operation of sustainable power grids with a high penetration of renewable energies requires new methods to analyze the dynamic stability. We provide new datasets of dynamic stability of synthetic power grids and find that graph neural networks (GNNs) are surprisingly effective at predicting the highly non-linear target from topological information only. To illustrate the potential to scale to real-sized power grids, we demonstrate the successful prediction on a Texan power grid model.
