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/

Zendy Plus

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

Zendy Tools

Zendy Open

Wind farms are a crucial driver toward the generation of ecological andrenewable energy. Due to their rapid increase in capacity, contemporary windfarms need to adhere to strict constraints on power output to ensure stabilityof the electricity grid. Specifically, a wind farm controller is required tomatch the farm's power production with a power demand imposed by the gridoperator. This is a non-trivial optimization problem, as complex dependenciesexist between the wind turbines. State-of-the-art wind farm control typicallyrelies on physics-based heuristics that fail to capture the full load spectrumthat defines a turbine's health status. When this is not taken into account,the long-term viability of the farm's turbines is put at risk. Given thecomplex dependencies that determine a turbine's lifetime, learning a flexibleand optimal control strategy requires a data-driven approach. However, as windfarms are large-scale multi-agent systems, optimizing control strategies overthe full joint action space is intractable. We propose a new learning methodfor wind farm control that leverages the sparse wind farm structure tofactorize the optimization problem. Using a Bayesian approach, based onmulti-agent Thompson sampling, we explore the factored joint action space forconfigurations that match the demand, while considering the lifetime ofturbines. We apply our method to a grid-like wind farm layout, and evaluateconfigurations using a state-of-the-art wind flow simulator. Our results arecompetitive with a physics-based heuristic approach in terms of demand error,while, contrary to the heuristic, our method prolongs the lifetime of high-riskturbines.

Scalable Optimization for Wind Farm Control using Coordination Graphs