A fast sequential transient stability preventive control approach driven by model interpretation

Abstract

Transient stability preventive control (TSPC), regarded as generation rescheduling, plays an important role in maintaining secure and economic dispatch of power systems. To accommodate the increasing uncertainty of operation conditions, online TSPC is urgently required in real power systems. To this end, a new sequential online TSPC strategy driven by transient stability assessment (TSA) model interpretation is proposed in this paper. In this strategy, two data-driven TSA models, namely full-feature-trained and controllable-feature-trained models, are deployed for online TSA and generation rescheduling, respectively. To identify the control generators for TSPC, an instance-based model interpretation tool, Local Interpretable Model-agnostic Explanations (LIME), is introduced to interpret the unstable operation detected by online TSA. In addition, a pair-wise generator regulation strategy and a continual prediction method are used to quickly search the TSPC operating point while ensuring the power shift balance of the system during regulation. The adaptability of the strategy to the online applications is demonstrated on the IEEE 3-machine 9-bus system and the IEEE 10-machine 39-bus system. • Sequential framework speeds up transient stability preventive control (TSPC). • TSA model trained by controllable features locates the origin of system instability. • Model interpretation breaks up the opacity of TSA model’s predictions.