A Data-driven Method for Transient Stability Margin Prediction Based on Security Region

Transient stability assessment (TSA) based on security region is of great significance to the security of power systems. In this paper, we propose a novel methodology for the assessment of online transient stability margin. Combined with a geographic information system (GIS) and transformation rules, the topology information and pre-fault power flow characteristics can be extracted by 2D computer-vision-based power flow images (CVPFIs). Then, a convolutional neural network (CNN)-based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency set. The network consists of two components: the classification network classifies the input samples into the credibly stable/unstable and uncertain categories, and the prediction network is utilized to further predict the generator stability indices of the categorized samples, which improves the network ability to distinguish between the samples with similar characteristics. The proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system, and the simulation results validate the effectiveness of the method.