# Stacked-GRU Based Power System Transient Stability Assessment Method  
[PERSON]  
1 State Grid Hunan Electric Power Company, Changsha 410000, China; [EMAIL_ADDRESS] (F.P.); [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (C.Z.);[EMAIL_ADDRESS] (X.J.); [EMAIL_ADDRESS] (L.L.)  
[PERSON]  
1 State Grid Hunan Electric Power Company, Changsha 410000, China; [EMAIL_ADDRESS] (F.P.); [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (C.Z.);[EMAIL_ADDRESS] (X.J.); [EMAIL_ADDRESS] (L.L.)  
[PERSON]  
2 School of Electrical Engineering, Wuhan University, Wuhan 430000, China;}[PERSON]IL_ADDRESS] [EMAIL_ADDRESS]; Tel.: +[EMAIL_ADDRESS]; Tel.: +[EMAIL_ADDRESS].  
In order to achieve rapid TSA, a large number of machine learning algorithms such as the BP neural network [12], support vector machine [13; 14], decision tree [15], random forest [16], and K-nearest neighbor [17] are applied in intelligent systems, such algorithms mainly use the key features of the power system after feature selection to establish functional relationship with transient stability. Once the fault is cleared, the transient stability assessment can be performed, but there is no guarantee of 100% accuracy of assessment. Therefore, an adaptive assessment method [9; 18] has emerged in recent years. By determining the time window of the fixed length after the fault, m data sampling points in the time window is used for training; the assessment is performed in chronological order until the assessment confidence meets the requirements. Such approaches guarantee the correctness of assessment, but the assessment time is long and the construction of the model is very cumbersome: m data sampling point means that m classifiers need to be trained. Although the authors of a past paper have [18] considered the influence of historical information on the current time assessment in the time series, it reduces the assessment time to a certain extent, but still needs to train a large number of classifiers.  
Aiming at solving the problems existing in the above adaptive assessment method, the main contribution of this paper is to proposes a stacked-GRU based intelligent TSA model, which realizes the parameter sharing at each moment through stacked-GRU and utilizes the time series data after the power system failure to train with less complex model structure. The offline training is performed to obtain the optimal parameters. When the application is online, it is judged whether the current assessment result satisfies the confidence level at the time of fault clearing, otherwise measurements will continue to be provided to the next moment for assessment until the confidence level is satisfied.  
On the one hand, it can perform TSA in the current moment, and on the other hand, can provide historical information for the assessment at the next moment when the assessment result at the current moment is unreliable, so there is no need to train multiple classifiers, and the problem of cumbersome training is solved. In addition, the method draws on the idea of deep learning [19] to use \"stacking\" to learn representational features from the input can further improve the accuracy of the classifier.  
The remainder of the paper is organized as follows. Section 2 introduces the structure of the proposed stacked-GRU. Section 3 proposes the transient stability intelligent assessment method. Simulations and analysis are carried out in Section 4. Finally, a conclusion is drawn in Section 5.