Electric Power System Transient Stability Assessment Based on Bi-LSTM Attention Mechanism

Abstract

This paper puts forward a Bi-LSTM attention mechanism model based on voltage phasor for electric power system transient stability assessment. The Bi-LSTM attention mechanism is used to map the relationship between voltage phasor and power system transient stability, and by establishing a sample matrix of transient stability information in the initial stage of the perturbation level, the extracted features are more robust, effectively reducing the false and missing samples, thus improving the generalization ability and evaluation performance of the model. Furthermore, by adjusting the network structure parameters of the best evaluation indicator. The mapping model between input features and transient stability is established to further reduce false positives and sample loss, and to improve the accuracy of network model evaluation. The improved model combines Bi-LSTM feature extraction layer and attention mechanism to form a hybrid model for a transient stability classification model, and the IEEE-39 bus New England test system is used to verify the accuracy of the model, and the wide-area noise is introduced into the generated data to evaluate the robustness of the system. Finally, the method is used to realize the transient stability evaluation of the electric power system based on voltage phasor data, and the validity of the proposed model is authenticated by comparative analysis of the proposed hybrid model with another deep learning models.