An Integrated Assessment Method for Power System Transient Stability Based on CBAM-ConvGRU

[Objective] To improve the effectiveness of transient stability assessment in power systems and address evaluation performance degradation under class-imbalance conditions，this paper proposes an integrated multi-task transient stability assessment method based on attention mechanisms and convolutional gated recurrent units （ConvGRU）. [Methods] The proposed approach combines ConvGRU with the convolutional block attention module （CBAM） to construct a comprehensive feature set representing transient rotor-angle stability and transient voltage stability. The traditional binary cross-entropy loss function is modified to dynamically adjust weights，guiding model-training process toward unstable samples. Additionally，the impact of classification decision thresholds on model performance is analyzed to determine optimal thresholds for transient stability evaluation，thereby reducing misclassification risks for critical instability events. [Results] Simulation results demonstrate that the proposed multi-task model，enhanced by the convolutional attention mechanism and modified loss function，significantly improves the accuracy of integrated assessments for transient rotor-angle stability and transient voltage stability，substantially reduces missed detections of unstable samples，and exhibits robust performance under class imbalance conditions. [Conclusions] By enhancing attention to critical features through spatial and channel dual-attention mechanisms，the proposed method achieves efficient integrated assessment of transient rotor-angle and voltage stability in power systems，providing new technical support for stable grid operation.