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Transient stability prediction under the concept of security region of a power system can be used to identify potential unstable states of the system and ensure its secure operation. In this paper, we propose a method to predict the transient stability margin under the concept of security region based on the long short-term memory (LSTM) network and attention mechanism (AM). This method can ensure rapid and accurate situational awareness of operators in terms of transient stability. The LSTM layer reduces the dimension of the historical steady-state power flow data, and the temporal characteristics are extracted from the data. Subsequently, the AM is introduced to differentiate the characteristics and historical transient stability margin data for the models to identify the information associated with stability. Finally, the LSTM and fully connected layers are used to predict the transient stability margin, providing up-to-date situational awareness of the power system to operators. We performed simulations on the IEEE 39-bus system, and the simulated results validated the effectiveness of the proposed method.

frontiers in energy research

Transient Stability Margin Prediction Under the Concept of Security Region of Power Systems Based on the Long Short-Term Memory Network and Attention Mechanism