Researchers at the U.S. National Renewable Energy Laboratory (NREL) have developed an AI-based tool that could significantly boost the efficiency and reduce the environmental impact of future wind farms.

The tool is called Wind Plant Graph Neural Network (WPGNN), which was trained on simulations of more than 250,000 randomly generated wind plant layouts under various atmospheric conditions, plant designs and turbine operations.

The AI used the simulation inputs to determine the optimal design of a wind plant, focusing on a strategy called wake steering.

NREL explains that wake steering is used to optimize the amount of energy produced by a plant “by controlling the wake moving from an upstream turbine away from a downstream turbine”.

The adoption of so-called ‘wake steering’ strategies could have a positive impact in terms of:

“Previously, site-specific wake steering optimization studies were very difficult, but the graph representation in the WPGNN dramatically improved our ability to represent flexible layouts, changing wind directions, and perform gradient-based optimization,” said Ryan King, co-author of the paper, Artificial Intelligence-Aided Wind Plant Optimization for Nationwide Evaluation of Land Use and Economic Benefits of Wake Steering.

The AI-guided scenario considered a nationwide deployment of 6,862 plant buildouts with a cumulative 721GW of generated power.

Researchers found that wake steering would yield varying benefits in different areas of the country. This study would therefore be useful in understanding how and where investments should be made in future projects.

Originally published on Power Engineering International.

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