A Classifier-Guided Diffusion Model-Based Key Sample Augmentation Method for Power System Transient Stability

Modern power systems are increasingly complex, and the risk of transient instability is rising accordingly. Data-driven transient stability assessment (TSA) is attractive for its efficiency, yet in practice the number of unstable events is much smaller than that of stable ones, leading to severe class imbalance and degraded accuracy. This paper proposes a SHAP-guided, classifier-controlled diffusion augmentation framework to mitigate imbalance and enhance TSA. First, SHAP analysis identifies critical unstable and near-boundary samples, ensuring that augmentation targets the most informative regions of the state space. Then, a classifier-guided conditional diffusion model—with a Transformer-based denoising network—generates class-faithful synthetic trajectories that capture long-range temporal dependencies and inter-variable couplings. Case studies on the IEEE 10-machine 39-bus system show that the proposed method consistently surpasses traditional over-sampling (e.g., SMOTE/ADASYN) and deep generative baselines (e.g., CGAN/TimeGAN) in terms of accuracy, precision, recall, and F 1-score. Moreover, the approach maintains strong performance under small-sample settings and shortened time-series inputs, demonstrating favorable adaptability and robustness. These results indicate that the proposed augmentation framework offers a practical and effective solution for TSA under severe class imbalance.