通过使用图形神经网络对电网拓的动态稳定性评估
Christian Nauck1, Michael Lindner1, Konstantin Schürholt2
1Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473 Potsdam, Germany.
Chaos (Woodbury, N.Y.)
|October 2, 2023
概括
图形神经网络 (GNN) 可以仅使用网络结构来预测电网的动态稳定性,从而提供计算效率高的解决方案. 这些模型展示了实际性能,并识别了电网漏洞,即使在较小的网络上进行训练.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 由于去中心化,惯性降低和生产波动性,日益增长的可再生能源整合挑战了电网动态稳定性.
- 对于大型电网而言,传统的动态稳定性模拟在计算上是难以处理的,而且成本过高.
- 图形神经网络 (GNN) 提供了一种有希望的方法来降低电网分析中的计算成本.
研究的目的:
- 开发和评估用于预测电网动态稳定的GNN模型.
- 为GNN研究创建和发布大量的合成电网动态稳定性的开源数据集.
- 评估GNN在电网内识别关键脆弱节点 ("问题制造者") 的有效性.
主要方法:
- 为动态稳定性分析生成大型合成电网数据集.
- 仅使用拓信息进行稳定性预测的图形神经网络 (GNN) 的应用.
- 在较小的电网上训练GNN模型,并在大型合成德克萨斯电网模型上测试它们的泛化.
主要成果:
- 仅从网络拓学来预测高度非线性动态稳定性目标,GNN表现出令人惊的有效性.
- 首次在动态稳定性预测中实现了实际用例性能.
- 在电网中成功识别了高精度的脆弱节点 ("问题制造者").
- 在小型电网上训练的GNN在大型合成电网上表现出准确的预测能力,这表明了强烈的泛化.
结论:
- 在计算上,GNN提供了一种高效和准确的方法来评估电网动态稳定性.
- 开发的GNN模型和数据集促进了电网稳定性分析的研究和开发.
- 这些发现突出了GNN在电力系统管理和稳定性评估中的实际应用潜力.
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