arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2201.09245eess.SYcs.LGcs.SY
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基于电网信息时空与拓扑嵌入深度神经网络的快速暂态稳定预测

Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network

Peiyuan Sun, Long Huo, Siyuan Liang, Xin Chen

AI总结:

本文提出TTEDNN模型,利用电网信息邻接矩阵融合结构与电气参数,从早期暂态动态时序数据中提取时空和拓扑特征,实现快速准确的暂态稳定预测,并在IEEE 39和118节点系统中验证了其最优鲁棒性和迁移能力。

摘要:

暂态稳定预测对于电力系统的快速在线评估和维持稳定运行至关重要。相量测量单元(PMU)的广泛部署推动了数据驱动的暂态稳定评估方法的发展。本文提出了时空与拓扑嵌入深度神经网络(TTEDNN)模型,利用早期暂态动态过程来预测暂态稳定性。TTEDNN模型通过从早期暂态动态的时间序列数据中提取时间和拓扑特征,能够准确高效地预测暂态稳定性。该模型采用电网信息邻接矩阵来融入电网结构参数和电气参数信息。在IEEE 39节点和IEEE 118节点电力系统中,使用单节点扰动和多节点扰动下的暂态动态仿真环境来测试TTEDNN模型的性能。结果表明,TTEDNN模型具有最优且最鲁棒的预测性能。此外,TTEDNN模型还展示了在更复杂的暂态动态仿真环境中预测暂态稳定性的迁移能力。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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