From Delaunay triangulation to topological data analysis: generation of more realistic synthetic power grid networks
提出一种基于德劳内三角剖分的随机模型来生成逼真的合成电网网络，并用拓扑数据分析的新指标验证其与真实电网的相似性，适用于IEEE测试案例和欧洲电网。
Abstract Assessing novel methods for increasing power system resilience against cyber-physical hazards requires real power grid data or high-quality synthetic data. However, for security reasons, even basic connection information for real power grid data are not publicly available. We develop a randomised model for generating realistic synthetic power networks based on the Delaunay triangulation and demonstrate that it captures important features of real power networks. To validate our model, we introduce a new metric for network similarity based on topological data analysis. We demonstrate the utility of our approach in application to IEEE test cases and European power networks. We identify the model parameters for two IEEE test cases and two European power grid networks and compare the properties of the generated networks with their corresponding benchmark networks.