Denseception network method for transient stability prediction of power systems

In the context of accelerated global energy transition, the high proportion of renewable energy grid connections and the proliferation of power control devices have significantly increased the tangled and haziness of the electromechanical transients in power grids, and the transient stability prediction has become an international forefront problem in the construction of smart grid security and defense system. However, existing methods face triple limitations: traditional physical models rely on ideal assumptions and are computationally inefficient; shallow data-driven models have insufficient feature extraction capabilities; and existing deep learning methods have poor generalization and lack interpretability. To manage the issues highlighted above, this study proposes a deep learning-based Denseception architecture and its accompanying data modeling method, which achieves a breakthrough in high-precision continuous numerical prediction of transient stability indicator (TSI) with engineering practicality. The heterogeneous multi-scale feature fusion network is constructed by integrating the DenseNet dense cross-layer connectivity, Xception deep separable convolution, and the dynamic weighting mechanism of the fully connected layers, which significantly improves the efficiency of the cross-scale dynamic feature extraction; and the three-channel two-dimensional spatial-temporal feature reconstruction method is innovatively designed, which reconstructs the temporal data of the whole fault process into an image-like structure, and combines with the adversarial training strategy to enhance the cross-topology generalization capability. The experiment reveals that the TSI prediction error of the Denseception model is prominently lower than that of the mainstream deep learning model in the IEEE 39–10 and 145–50 systems, which is the best performance. This study overcomes the contradiction between speed, accuracy, and generalizability of traditional methods, provides a full chain solution for the dynamic security defense of a high percentage new energy power grids, and provides a critical time window for emergency control.