A data-driven method for online transient stability monitoring with vision-transformer networks

Online transient stability assessment (TSA) plays an important role in power system planning and operation. The massive integration of renewable energy sources into the grids increases the risks of relay malfunction; however, the existing TSA approaches only apply to scenarios where the fault could be successfully cleared, and are not applicable under relay failure scenarios. Besides, the post-fault TSA methods normally rely on the accurate fault clearance information, which would be affected by the clock synchronization errors in relays. Thus, this paper proposes an online monitoring system that assesses transient stability based on the current operating condition, which is independent of the accurate moments of fault occurrence and clearance provided by fault indicators or relays signals; the transient stability prediction during the fault duration provides the system operators with an early warning of system instability in the event of relay failures. Moreover, an adaptively adjusted criterion for instability is proposed to balance the false alarm and misclassification rates. Furthermore, novel Vision-Transformer-based models for both TSA and unstable generators identification are built in this paper. Compared with other networks, the self-attention structure in the Transformer leads to a global receptive field and higher resolution for capturing time-series information in each sample. Our experiments on the IEEE-39 system show that the proposed method can assess transient stability at each moment in different operation states. It outperforms existing machine learning-based models in terms of accuracy and robustness in TSA and unstable generators identification tasks.