Generative Adversarial Networks for Real-time Stability of Inverter-based Systems

TL;DR

This paper introduces a scalable, real-time stability assessment method for inverter-based systems using conditional GANs, improving efficiency over traditional methods in islanded power systems.

Contribution

It presents a novel application of cGANs for real-time stability estimation, offering a scalable and efficient alternative to conventional methods in power systems.

Findings

cGANs can learn power system stability characteristics

The method provides real-time stability assessment in LV distribution systems

Demonstrated advantages include scalability and reduced computational complexity

Abstract

In islanded systems with droop-controlled sources, the droop coefficients need to be tuned in real-time using supervisory control to maintain asymptotic stability. In contrast to offline tuning methods, online domain-of-stability estimation yields non-conservative droop gains in real-time, ensuring good power sharing performance as the operating point varies. The challenge in the conventional online domain-of-stability estimation process is its unscalability and high computational complexity. In this paper, an efficient alternative using conditional Generative Adversarial Networks (cGANs) is described. We demonstrate that the notion of power system stability can be learned by such deep neural networks, and that they can offer a scalable alternative to conventional domain-of-stability estimation methods in islanded distribution systems. The implementation of cGANs-based stability assessment is described for an LV distribution test case and its advantages demonstrated.