WindESCo Swarm enables turbines to ‘cooperatively adjust positioning’ and is being installed at three US sites
US outfit WindESCo has launched a software system for autonomous, cooperative control of wind assets.
WindESCo Swarm enables turbines to cooperatively adjust positioning to boost production for the entire wind farm by 3-5% annually.
For a typical 1GW wind plant, this would translate to in excess of $20 million (€17 million) over a five-year period, the company calculated.
The first commercial implementation on three wind plants with over 300MW of capacity is underway in North America.
It is being offered as a repowering solution that is compatible with most turbine makes and models.
WindESCo Swarm is the first off-the-shelf solution that “connects and creates a shared understanding between turbines”, the company said.
It allows turbines to “know what is happening” at neighbouring assets, along with the direction, strength and any shifts in wind resource, to change their operational profiles and “optimise the swarm” instead of individual machines.
By understanding how their operation is impacting the performance of the site at large, WindESCo Swarm enables wind assets to take predictive, protective and proactive turbine control measures for maximiSed fleet-wide production.
WindESCo CEO Blair Heavey said: “Wind projects often see low profit margins, especially when plants fail to meet performance projections, but current solutions do not provide actionable insights and a measurable RoI in terms of tangible site-wide production boosts.
“WindESCo Swarm is set to become the go-to solution for wind operators looking to supercharge their assets and deliver consistent, year-on-year revenue increases without compromising useful asset life – in short, squeezing out every megawatt available from their investments.”
WindESCo Swarm combines hardware and software as an integrated system to help owners unlock value by allowing turbines to communicate with and learn from each other.
To develop the system WindESCo used a multidisciplinary approach, combining the fields of turbine loads, controls, meteorology, sensing and machine learning.
The system has been developed with three years of concentrated investment.