基于时空特征融合的电力系统暂态稳定评估

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doi:10.16088/j.issn.1001-6600.2024022402

摘要： 为提高暂态评估模型对电气动态特征的提取能力,以及面临系统拓扑结构发生变化时的泛化能力,本文提出一种具有时空双通道并行结构的在线评估模型。首先,该模型基于门控循环单元(GRU)捕捉暂态时序数据的动态信息,基于图注意力网络(GAT)构建电力系统拓扑结构与暂态稳定状态的非线性拟合关系,并通过注意力机制融合两通道的时空特征,从而得到更可靠的评估结果。其次,将该模型与迁移学习技术相结合,当原系统拓扑结构发生变化后,更新模型的网络参数,实现模型的在线更新。最后,采用IEEE 39节点系统和IEEE 300节点系统进行仿真与验证,模型评估准确率分别达到98.62%和98.51%,表明所提方法能够实现高效的暂态稳定评估,且有较强的鲁棒性。

关键词: 电力系统, 暂态稳定评估, 深度学习, 时空特征, 注意力特征融合, 迁移学习

Abstract: In order to improve the transient assessment model’s ability to extract electrical dynamic features and its generalization ability when facing changes in system topology, this paper proposes an online assessment model with a temporal and spatial dual-channel parallel structure. Firstly, the dynamic information of the transient time series data of the model is captured by the Gated Recurrent Unit (GRU). The nonlinear fitting relationship between power system topology and transient stability state is constructed based on the Graph Attention Network (GAT). And the spatial and temporal features of the two channels are fused through the attention mechanism to obtain more reliable evaluation results. Then, when the topology of the original system changes, the model is combined with transfer learning technology to update the network parameters of the model and realize the online update of the model. Finally, through the simulation and verification of IEEE 39-node system and IEEE 300-node system, the model evaluation accuracy reaches 98.62% and 98.51%, respectively. The results show that the proposed method can realize efficient transient stability evaluation and has strong robustness.

Key words: electric power system, transient stability assessment, deep learning, spatiotemporal feature, attention feature fusion, transfer learning

中图分类号: TM712

李欣, 宁静. 基于时空特征融合的电力系统暂态稳定评估[J]. 广西师范大学学报（自然科学版）, 2024, 42(6): 89-100.

LI Xin, NING Jing. Online Assessment of Transient Stability in Power Systems Based on Spatiotemporal Feature Fusion[J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(6): 89-100.

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