Model-Free Power System Stability Enhancement with Dissipativity-Based Neural Control

TL;DR

This paper introduces a model-free neural control method based on dissipativity principles to improve power system stability without relying on detailed grid models.

Contribution

It develops a neural network-based controller that learns dissipativity matrices from data, enhancing transient stability in power systems with virtual synchronous generators.

Findings

Neural controllers improve stability in a two-area power system model.

The approach does not require explicit grid dynamic models.

Numerical results validate the effectiveness of the method.

Abstract

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.