Finding a Closest Saddle-Node Bifurcation in Power Systems: An Approach by Unsupervised Deep Learning

We propose a neural network using an unsupervised learning strategy for direct computation of closest saddle-node bifurcations, eliminating the need for labeled training data. Our method not only estimates the worst-case load increase scenarios but also significantly reduces the computational complexity traditionally associated with this task during inference time. Simulation results validate the effectiveness and real-time applicability of our approach, demonstrating its potential as a robust tool for modern power system analysis.