Transient stability assessment (TSA) based on artificial intelligence typically has two distinct model management approaches: a unified management approach for all faulted lines and a separate management approach for each faulted line.To address the shortcomings of the aforementioned approaches

namely accuracy

training time

and model management complexity

a multi-model management approach for power system TSA based on multi-moment feature clustering has been proposed.First

the steady-state and transient features present under fault conditions were obtained through a transient simulation of line faults.The input sample set was then constructed using the aforementioned multi-moment electrical features and the embedded faulty line numbers.Subsequently

K-means clustering was conducted on each line based on the similarity of their electrical features

employing t-SNE dimensionality reduction.The PSO-CNN model was trained separately for each cluster to generate several independent TSA models.Finally

a model effectiveness evaluation system consisting of five metrics was established

and the effect of the sample imbalance ratio on the model effectiveness was investigated.The model effectiveness was evaluated using the IEEE 39-bus system algorithm.The results showed that the multi-model management strategy based on multi-moment feature clustering can effectively combine the two advantages of superior evaluation performance and streamlined model management by fully extracting system features.Moreover

this approach allows for more flexible adjustments to line topology changes.