AUTHOR=An Jun , Zhang Liang , Zhou Yibo , Yu Jiachen 

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

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.838791

DOI=10.3389/fenrg.2022.838791

ISSN=2296-598X

ABSTRACT=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. 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 operation time-series characteristics are extracted from the data. Subsequently, the attention mechanism is introduced to differentiate the characteristics and historical transient stability margin data for the models to identify parameters associated with the 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 designed and performed simulations using the IEEE 39-bus system, and the simulated results validate the effectiveness of the proposed method.