关键词: 电力系统, 空间拓扑信息, 时空轨迹预测, 暂态稳定, 栈式长短期记忆神经网络

Abstract: With the development of wide area measurement technology,it is important to identify transient stability in advance and take preventive control measures for safety and stability of power systems,and the spatio-temporal trajectory prediction of power systems is key.Although the traditional method of space-time trajectory prediction of power grid without system model does not depend on system model and its calculation speed is faster,the spatial topology of power grid cannot be considered in the process of prediction.In addition,in the big data environment of modern complex power grid,prediction accuracy with system model still needs to be improved compared with the method of using deep learning.Therefore,this paper proposed a space-time trajectory prediction model based on two-layer staclong short term memory and neighborhovd relationswhips.It adopts stacked long short term memory neural network,and introduces the characteristics of the first and second order nodes of the generator to be predicted into the model.The experimental results show that the root mean square error of the prediction decreases gradually for the prediction accuracy increases gradually with the support vector regression method,the recurrent neural network method,the single-layer long shortterm memory neural network method and space-time trajectory prediction methodbased on the double layers stacked long short term memory on the test set.When first-order node and second-order node are respectively introduced into space-time trajectory prediction of power grid,the prediction accuracy increases as the introduction of adjacent nodes increases.Compared with the traditional method of power grid spatio-temporal trajectory prediction,the model based on double layer stacked long short term memory and the topological relationship of neighboring nodes can better characterize the change of power grid space-time trajectory under transient scenarios,and achieve the prediction of power grid spatio-temporal trajectory more accurately.

Key words: Powersystem, Spatial topological information, Spatio-temporal trajectory prediction, Stacked long short term memory neural network, Transient stability