English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Transient stability of power systems is becoming increasingly importantbecause of the growing integration of renewable resources. These resources leadto a reduction in mechanical inertia but also provide increased flexibility infrequency responses. Namely, their power electronic interfaces can implementalmost arbitrary control laws. To design these controllers, reinforcementlearning (RL) has emerged as a powerful method in searching for optimalnon-linear control policy parameterized by neural networks.  A key challenge is to enforce that a learned controller must be stabilizing.This paper proposes a Lyapunov regularized RL approach for optimal frequencycontrol for transient stability in lossy networks. Because the lack of ananalytical Lyapunov function, we learn a Lyapunov function parameterized by aneural network. The losses are specially designed with respect to the physicalpower system. The learned neural Lyapunov function is then utilized as aregularization to train the neural network controller by penalizing actionsthat violate the Lyapunov conditions. Case study shows that introducing theLyapunov regularization enables the controller to be stabilizing and achievesmaller losses.

Lyapunov-Regularized Reinforcement Learning for Power System Transient Stability