Interpretable Time-Adaptive Transient Stability Assessment Based on Dual-Stage Attention Mechanism

Fast and reliable transient stability assessment (TSA) is significant for safe and stable power system operation. Deep learning provides a new tool for TSA. However, it is difficult to apply the TSA models based on deep learning practically because of their inexplicability. Therefore, an interpretable time-adaptive model based on a dual-stage attention mechanism and gated recurrent unit (GRU) is proposed for TSA. A feature attention block and a time attention block are included in the dual-stage mechanism to explain the TSA rules learned by the proposed TSA model. Meanwhile, interpretability is utilized to guide the optimization of the TSA model. Firstly, the measurements are input into the feature attention block to calculate feature attention factors. Then, the measurements weighted by the feature attention factors are input into a GRU block for further abstracting. The abstracted features are input into the time attention block to obtain time attention factors. Finally, the abstracted features weighted by the time attention factors are sent into fully connected layers for TSA. To achieve time-adaptive TSA, multiple channels are constructed to process the features at different decision moments. The performance of the proposed model is verified in the IEEE-39 bus system and a realistic regional system.