Siamese recurrent neural networks for the robust classification of grid disturbances in transmission power systems considering unknown events

The automated identification and localisation of grid disturbances is a major research area and key technology for the monitoring and control of future power systems. Current recognition systems rely on sufficient training data and are very error‐prone to disturbance events, which are unseen during training. This study introduces a robust Siamese recurrent neural network using attention‐based embedding functions to simultaneously identify and locate disturbances from synchrophasor data. Additionally, a novel double‐sigmoid classifier is introduced for reliable differentiation between known and unknown disturbance types and locations. Different models are evaluated within an open‐set classification problem for a generic power transmission system considering different unknown disturbance events. A detailed analysis of the results is provided and classification results are compared with a state‐of‐the‐art open‐set classifier.