  To support N-1 pre-fault transient stability assessment, this paper
introduces a new data collection method in a data-driven algorithm
incorporating the knowledge of power system dynamics. The domain knowledge on
how the disturbance effect will propagate from the fault location to the rest
of the network is leveraged to recognise the dominant conditions that determine
the stability of a system. Accordingly, we introduce a new concept called
Fault-Affected Area, which provides crucial information regarding the unstable
region of operation. This information is embedded in an augmented dataset to
train an ensemble model using an instance transfer learning framework. The test
results on the IEEE 39-bus system verify that this model can accurately predict
the stability of previously unseen operational scenarios while reducing the
risk of false prediction of unstable instances compared to standard approaches.
