Power outage scenarios generation and evaluation are the foundation of intelligence restoration strategy development, which further strengthens the self-healing capabilities of the power system. However, few studies have explicitly focused on power outage scenarios, and a complete procedure has yet to be established. Based on the Multi-head Attention for Tabular data-Denoising Diffusion Probabilistic Model (MAT-DDPM), an extension method for power outage scenarios is presented in the paper. First, basic power outage scenarios are generated based on a whole-process power outage simulation scheme. Then, MAT-DDPM is proposed to rapidly generate diverse power outage scenarios, refining the DDPM structure by designing an input module and integrating a multi-head attention mechanism. Finally, an enhancement process for the large language model is designed to evaluate outage scenarios, thereby generating analysis reports and providing feedback. Simulation results confirm that the outage scenarios generated by MAT-DDPM are diverse and satisfy the power system operating constraints with high probability. The evaluation model provides valuable analytical insights into power outage scenarios, helping operators systematically understand critical outage information.

To investigate impact of time delays on the small signal stability of power systems, the least-damped eigenvalues with the smallest damping ratios have been calculated by eigen-analysis methods based on Solution Operator Discretization (SOD) with Pseudo-Spectral collocation (PS) and Implicit Runge-Kutta (IRK) methods. This paper evolves SOD-PS/IRK into their partial counterparts, i.e., PSOD-PS/IRK, with greatly enhanced efficiency and reliability in analyzing large-scale time delayed power systems. Compared with SOD-PS/IRK, PSOD-PS/IRK are characterized in constructing low order discretization matrices of solution operator as well as efficiently and directly solving the embedded Matrix-Inverse-Vector Products (MIVPs). The dimensions of the discretization matrices of solution operator are largely reduced as only the retarded state variables are discretized, rather than all state variables as in SOD-PS/IRK. Meanwhile, the proposed PSOD-PS/IRK optimize the most computationally expensive operations in SOD-PS/IRK by avoiding the iterative solutions to the two embedded MIVPs. PSOD-PS/IRK directly and efficiently compute the MIVPs via factorizing the Kronecker product-like discretization matrices of the solution operator into Schur complements. The Central China-North China ultra-high-voltage power grid with 80577 state variables serves to validate the proposed PSOD-PS/IRK and shows that compared with SOD-PS/IRK, the computational time consumed by PSOD-PS/IRK is cut down by 49.96 times without loss of any accuracy.