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

The increasing penetration of renewables in distribution networks calls forfaster and more advanced voltage regulation strategies. A promising approach isto formulate the problem as an optimization problem, where the optimal reactivepower injection from inverters are calculated to maintain the voltages whilesatisfying power network constraints. However, existing optimization algorithmsrequire the exact topology and line parameters of underlying distributionsystem, which are not known for most cases and are difficult to infer. In thispaper, we propose to use specifically designed neural network to tackle thelearning and optimization problem together. In the training stage, the proposedinput convex neural network learns the mapping between the power injections andthe voltages. In the voltage regulation stage, such trained network can findthe optimal reactive power injections by design. We also provide a practicaldistributed algorithm by using the trained neural network. Theoretical boundson the representation performance and learning efficiency of proposed model arealso discussed. Numerical simulations on multiple test systems are conducted toillustrate the operation of the algorithm.

Input Convex Neural Networks for Optimal Voltage Regulation