Research on Transient Stability Model of Power System Based on Periodic Enhanced Informer

In view of the nonlinearity and complexity of power system connection, it is difficult to directly identify the key characteristics of power system stability, while there are characteristics unrelated to system stability. For this reason, this paper proposes a method to distinguish the transient stability margin of power grid based on long short memory recurrent network. This method uses unbalanced data clustering algorithm to perform clustering analysis on the historical data set, extract a variety of typical instability characteristics, and consider the periodic characteristics of the unstable sequence. The unstable value in the same period of the input sequence is combined with the output of the long and short term memory network (LSTM) network to output the instability prediction results. At the same time, the key value of the periodic instability in the input sequence is connected in series with the output of the Informer model to build a full connection layer, and combined with the convolutional neural network to build a power system transient stability model based on periodically enhanced Informer. Experimental results show that the model effectively solves the problem of periodic mode attenuation of non‐stationary load series and alleviates the redundancy problem of key features of power grid stability by building a multi‐dimensional periodic feature extraction channel.