Imagine a smart brain for a wind farm that looks at everything from weather to machine wear and tear all at once. Instead of adjusting turbines one by one, it coordinates the whole fleet to get the most energy while keeping the machines healthy. It's like upgrading from a basic thermostat to a fully automated smart home for giant wind turbines.
Wind farm operators struggle to balance maximum energy production with machine wear and environmental impact because data is fragmented and control systems are often disconnected.
An open-source, AI-driven decision support environment and a data management toolbox for wind power asset management.
If you are a wind farm operator dealing with inefficient turbine coordination and high maintenance costs — this project developed an AI-driven decision support environment that optimizes system-wide performance and asset life.
If you are a software provider dealing with fragmented data from different turbine brands — this project developed an open-source data management toolbox that integrates multi-source and multi-format data for better analytics.
If you are a consultancy dealing with the social and environmental impact of energy sites — this project developed a tool to assess the socio-economic and environmental impact of different wind farm operation modes.
Based on available project data, the TWAIN decision support environment and its toolboxes are being developed as open-source, meaning the core software is intended to be freely available.
Yes, the project is designed for wind power asset management across entire wind farms and expects more than 1000 engineers to use the tools within the next decade.
The project explicitly states it is developing an open-source environment and open-source data management toolboxes.
It uses a digital environment architected for multi-source and multi-format data integration to connect with turbines and components at various life stages.
The project runs from 2023-11-01 to 2027-10-31, with deliverables including an initial version of the automated environment with security constraints.
The consortium is heavily industry-weighted, with 9 industrial partners (69% ratio) including 3 SMEs, ensuring the AI tools are grounded in commercial reality. The collaboration spans 8 European countries, combining the academic expertise of 3 universities and 1 research center with practical operational experience from the wind energy sector.
Contact Danmarks Tekniske Universitet (DTU) regarding the TWAIN open-source toolboxes.
Contact us to find the right industrial partner for AI-driven wind asset management.
