Rapid N-1 security assessment in modern power systems faces a critical conflict between computational timeliness and the heavy reliance on labeled data for high-fidelity models. To mitigate this issue, a unified framework co-optimizing a physics-informed neural network (PINN) and memory-resident computing is proposed. At the algorithm level, power flow equation residuals are incorporated into the PINN formulation as physical regularization terms. This integration facilitates better alignment with electrical constraints and improves generalization capabilities under small-sample conditions. At the system level, a heterogeneity-aware asynchronous parallel computing architecture is developed. In this architecture, pull-based scheduling and lock-free memory mapping are utilized to mitigate straggler effects, thereby reducing synchronization latency and I/O overhead. Numerical case studies on the IEEE 39-bus system demonstrate that the physics mismatch is reduced by nearly two orders of magnitude compared to a baseline deep neural network (DNN), and the total execution time for scanning 20,000 contingencies is decreased by 34.0%.
Hua et al. (Mon,) studied this question.