Model-Free Power System Stability Enhancement with Dissipativity-Based Neural Control
This addresses stability issues in modern power grids for operators and engineers, but it is incremental as it builds on existing dissipativity and neural control methods.
The paper tackled transient stability challenges in power systems with converter-interfaced generation by proposing a model-free, dissipativity-based neural controller for virtual synchronous generators, achieving enhanced stability as validated on a modified Kundur two-area system.
The integration of converter-interfaced generation introduces new transient stability challenges to modern power systems. Classical Lyapunov- and scalable passivity-based approaches typically rely on restrictive assumptions, and finding storage functions for large grids is generally considered intractable. Furthermore, most methods require an accurate grid dynamics model. To address these challenges, we propose a model-free, nonlinear, and dissipativity-based controller which, when applied to grid-connected virtual synchronous generators (VSGs), enhances power system transient stability. Using input-state data, we train neural networks to learn dissipativity-characterizing matrices that yield stabilizing controllers. Furthermore, we incorporate cost function shaping to improve the performance with respect to the user-specified objectives. Numerical results on a modified, all-VSG Kundur two-area power system validate the effectiveness of the proposed approach.