标题： 基于图神经网络的可持续电力系统动态稳定性分析 显示英文标题

标题： Towards dynamic stability analysis of sustainable power grids using graph neural networks

摘要： 为了缓解气候变化，可再生能源的份额需要提高。 由于去中心化、惯性减少和生产波动，可再生能源给电网带来了新的挑战。 在可再生能源渗透率高的情况下，可持续电网的运行需要新的方法来分析动态稳定性。 我们提供了合成电网动态稳定性的新数据集，并发现图神经网络（GNNs）仅从拓扑信息中就能惊人地有效地预测高度非线性目标。 为了说明扩展到实际规模电网的潜力，我们展示了在德克萨斯电网模型上的成功预测。 显示英文摘要

摘要： 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.