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

2019/01/16 by Xilei Cao, Cao, Xilei, Gurupraanesh Raman +5 · 1 citation
Engineering · #Power System Optimization and Stability #Islanding Detection in Power Systems #Optimal Power Flow Distribution

paper · pdf · doi:10.48550/arxiv.1901.05114

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.

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