  Balanced data is required for deep neural networks (DNNs) when learning to
perform power system stability assessment. However, power system measurement
data contains relatively few events from where power system dynamics can be
learnt. To mitigate this imbalance, we propose a novel data augmentation
strategy preserving the dynamic characteristics to be learnt. The augmentation
is performed using Variational Mode Decomposition. The detrended and the
augmented data are tested for distributions similarity using Kernel Maximum
Mean Discrepancy test. In addition, the effectiveness of the augmentation
methodology is validated via training an Encoder DNN utilizing original data,
testing using the augmented data, and evaluating the Encoder's performance
employing several metrics.
