Information entropy based prioritization strategy for data-driven transient stability batch assessment

Transient stability batch assessment (TSBA) is essential for dynamic security check in both power system planning and day-ahead dispatch. It is also a necessary technique to generate sufficient training data for data-driven online transient stability assessment (TSA). However, most existing work suffers from various problems including high computational burden, low model adaptability, and low performance robustness. Therefore, it is still a significant challenge in modern power systems, with numerous scenarios (e.g., operating conditions and "N-k" contingencies) to be assessed at the same time. The purpose of this work is to construct a data-driven method to early terminate timedomain simulation (TDS) and dynamically schedule TSBA task queue a prior, in order to reduce computational burden without compromising accuracy. To achieve this goal, a time-adaptive cascaded convolutional neural networks (CNNs) model is developed to predict stability and early terminate TDS. Additionally, an information entropy based prioritization strategy is designed to distinguish informative samples, dynamically schedule TSBA task queue and timely update model, thus further reducing simulation time. Case study in IEEE 39-bus system validates the effectiveness of the proposed method.