  Online identification of post-contingency transient stability is essential in
power system control, as it facilitates the grid operator to decide and
coordinate system failure correction control actions. Utilizing machine
learning methods with synchrophasor measurements for transient stability
assessment has received much attention recently with the gradual deployment of
wide-area protection and control systems. In this paper, we develop a transient
stability assessment system based on the long short-term memory network. By
proposing a temporal self-adaptive scheme, our proposed system aims to balance
the trade-off between assessment accuracy and response time, both of which may
be crucial in real-world scenarios. Compared with previous work, the most
significant enhancement is that our system learns from the temporal data
dependencies of the input data, which contributes to better assessment
accuracy. In addition, the model structure of our system is relatively less
complex, speeding up the model training process. Case studies on three power
systems demonstrate the efficacy of the proposed transient stability assessment
system.
