Stability Manifold framework maps grid risks with 50 scenarios

As renewable energy and inverter-based resources (IBRs) increasingly replace traditional synchronous generators, power system stability becomes harder to predict. In their paper, Conte et al. introduce stability manifolds—a method to map out entire regions of controller parameters that keep the system stable under multiple operating conditions. They avoid brute-force simulations by using probabilistic support vector machine classification to adaptively sample the parameter space, approximating stability boundaries with far fewer computations. Surrogate optimization then finds initial controller settings that satisfy both bandwidth and phase-margin constraints.

The framework was validated on a modified Cigré European HV network benchmark with 50 distinct operating scenarios and growing inverter penetration. Key findings: stability sensitivity grows sharply as inverter share increases, interactions among multiple IBRs reshape admissible parameter regions in complex ways, and simplified equivalent-network models can overlook critical system-level limitations—meaning utilities need full-network models to ensure safe operation. This work offers a practical path to stability-oriented controller design and interconnection studies in real-world converter-dominated grids.