论文标题
使用网格信息的时间和拓扑嵌入深神经网络的快速瞬态稳定性预测
Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network
论文作者
论文摘要
瞬态稳定性预测对于快速的在线评估和维护电力系统中的稳定操作至关重要。相体测量单元（PMU）的广泛部署促进了数据驱动的瞬态稳定性评估方法的发展。本文提出了时间和拓扑嵌入深神经网络（TTEDNN）模型，以预测与早期瞬态动力学的瞬态稳定性。 TTEDNN模型可以通过从早期瞬态动力学的时间序列数据中提取时间和拓扑特征来准确有效地预测瞬态稳定性。网格信息的邻接矩阵用于合并电网结构和电参数信息。单个节点和多节点扰动下的瞬态动力学仿真环境用于测试IEEE 39-BUS和IEEE 118-BUS POWER系统的TTEDNN模型的性能。结果表明，TTEDNN模型具有最佳，最强大的预测性能。此外，TTEDNN模型还展示了传递能力，以预测更复杂的瞬态动力学仿真环境中的瞬态稳定性。
Transient stability prediction is critically essential to the fast online assessment and maintaining the stable operation in power systems. The wide deployment of phasor measurement units (PMUs) promotes the development of data-driven approaches for transient stability assessment. This paper proposes the temporal and topological embedding deep neural network (TTEDNN) model to forecast transient stability with the early transient dynamics. The TTEDNN model can accurately and efficiently predict the transient stability by extracting the temporal and topological features from the time-series data of the early transient dynamics. The grid-informed adjacency matrix is used to incorporate the power grid structural and electrical parameter information. The transient dynamics simulation environments under the single-node and multiple-node perturbations are used to test the performance of the TTEDNN model for the IEEE 39-bus and IEEE 118-bus power systems. The results show that the TTEDNN model has the best and most robust prediction performance. Furthermore, the TTEDNN model also demonstrates the transfer capability to predict the transient stability in the more complicated transient dynamics simulation environments.
