随着互联电网规模的扩大,扰动后电力系统频率响应的时空分布特征变得愈发明显,准确预测电网频率响应可以为电网安全控制提供有效支撑.然而,传统基于模型的频率响应预测方法需要建立精确的电力系统元件模型,其计算时间长,无法满足在线运行的要求,数据驱动的方法以其计算快速性和不依赖与模型的特性得到了广泛应用.因此,本文提出了一种基于图卷积神经网络(graph convolutional network,GCN)和长短期记忆网络(a long short-term memory,LSTM)组成的时空网络的频率预测模型.在所提方法中,来自同步相量测量单元测量的数据作为时空网络输入,使用嵌入电网拓扑信息的改进GCN提取空间维度特征以及LSTM提取时间维度特征.进一步,利用滚动更新的方式,训练时空网络回归模型,实现异步频率序列的预测.在IEEE 39节点和IEEE 118节点系统上验证了所提方法的有效性和抗噪性.“,”Owing to the expansion of the grid interconnection scale, the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important. These characteristics can provide effective support in coordinated security control. However, traditional model-based frequency-prediction methods cannot satisfactorily meet the requirements of online applications owing to the long calculation time and accurate power-system models. Therefore, this study presents a rolling frequency-prediction model based on a graph convolutional network (GCN) and a long short-term memory (LSTM) spatiotemporal network and named as STGCN-LSTM. In the proposed method, the measurement data from phasor measurement units after the occurrence of disturbances are used to construct the spatiotemporal input. An improved GCN embedded with topology information is used to extract the spatial features, while the LSTM network is used to extract the temporal features. The spatiotemporal-network-regression model is further trained, and asynchronous-frequency-sequence prediction is realized by utilizing the rolling update of measurement information. The proposed spatiotemporal-network-based prediction model can achieve accurate frequency prediction by considering the spatiotemporal distribution characteristics of the frequency response. The noise immunity and robustness of the proposed method are verified on the IEEE 39-bus and IEEE 118-bus systems.