基于图卷积网络的快速暂态安全评估方法

快速、可靠的电力系统动态安全评估能够显著提高电力系统运行方式优化调整的效率。针对电力系统暂态稳定预想事故扫描需要完成大量仿真、过于耗时的问题，提出了基于图卷积网络的快速动态安全分析方法。该方法基于电力系统的潮流特征和拓扑特征构建电力系统潮流特征图。利用图卷积方法对电力系统运行状态进行特征挖掘与特征学习，将动态安全评估问题建模为图上节点分类问题。所得模型在读取电网拓扑与潮流运行状态后，仅须完成一次前向计算即可同时给出预想事故集中多个预想事故的稳定性预测结果，无须依赖仿真波形或量测数据，实现快速暂态稳定预想事故扫描。IEEE39节点系统算例测试表明，算法设计正确、高效、准确率高，能够显著提高暂态稳定预想事故扫描的效率，实现快速动态安全评估。

Fast transient security assessment based on graph neural networks

Fast and reliable power system dynamic security assessment can significantly improve the efficiency of power system operating state optimization. Power system contingency scanning requires massive simulations. These are way too time-consuming. Given this, a graph convolutional network (GCN)-based fast dynamic security assessment method is proposed. A power flow feature graph is established based on power flow features and network topology. GCNs are used to extract and learn the features of power system operating states. The dynamic security assessment problem is modeled as the classification problem of nodes in the graph. After the power network topology and the power flow state are input into the model, it only needs to conduct forward calculation one time to provide the stability prediction results of all the contingencies in the anticipated contingency set. Fast contingency scanning is realized as no simulation results nor measurement data are required. Test results are gained in the IEEE39-node system, proving the correctness, efficiency, and accuracy of the proposed method. The efficiency of the contingency scanning process is greatly improved to realize fast dynamic security assessment. This work is supported by the National Natural Science Foundation of China (No. 52107104).