全球能源互联网（英文）2022，Vol.5Issue(1)：96-107,12.DOI:10.14171/j.2096-5117.gei.2022.01.008
基于GCN-LSTM时空网络的电力系统扰动后频率响应预测方法
GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems
摘要
Abstract
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.关键词
同步相量量测/频率响应预测/时空分布特性/改进图卷积神经网络/长短期记忆网络/时空网络Key words
Synchronous phasor measurement/Frequency-response prediction/Spatiotemporal distribution characteristics/Improved graph convolutional network/Long short-term memory network/Spatiotemporal-network structure引用本文复制引用
黄登一,刘灏,毕天姝,杨奇逊..基于GCN-LSTM时空网络的电力系统扰动后频率响应预测方法[J].全球能源互联网（英文）,2022,5(1):96-107,12.基金项目
This work was supported by the National Natural Science Foundation of China(Grant Nos.51627811,51725702)and the Science and Technology Project of State Grid Corporation of Beijing(Grant No.SGBJDK00DWJS2100164). （Grant Nos.51627811,51725702）
