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

Microgrids have more operational flexibilities as well as uncertainties thanconventional power grids, especially when renewable energy resources areutilized. An energy storage based feedback controller can compensate undesireddynamics of a microgrid to improve its stability. However, the optimal feedbackcontrol of a microgrid subject to a large disturbance needs to solve aHamilton-Jacobi-Bellman problem. This paper proposes a machine learning-basedoptimal feedback control scheme. Its training dataset is generated from alinear-quadratic regulator and a brute-force method respectively addressingsmall and large disturbances. Then, a three-layer neural network is constructedfrom the data for the purpose of optimal feedback control. A case study iscarried out for a microgrid model based on a modified Kundur two-area system totest the real-time performance of the proposed control scheme.

Machine Learning based Optimal Feedback Control for Microgrid Stabilization