A self-attention-embedded deep learning model for phasor measurement unit-based post-fault transient stability prediction

Abstract

Although deep learning-based predictors have achieved high accuracy in phasor measurement units (PMUs)-based post-fault transient stability assessment (TSA), most of these “black-box” models are not interpretable, making it difficult for operators to select proper countermeasures for instability prevention. To address this problem, a novel deep learning model embedded with self-attention layers is firstly proposed for TSA. After that, a transfer learning strategy is further proposed to develop a set of predictors aiming at the identification of unstable generators. Case study on the New England to-machine 39-bus system shows that, compared with other baseline models, the proposed self-attention-embedded model is able to achieve better performance in transient stability classification. Moreover, together with the embedded attention module, the predictors generated by transfer learning can be used to inform the operators about the cluster of the unstable generators in the disturbed power system.