Analytics for asset Integrity Management of Windfarms (AIMWind)
What we do
About 65 GW of onshore wind turbine installations in Europe will reach end-of-design-life by 2028. It is time for the operators to decide on one of the three end-of-life scenarios, namely, decommissioning, lifetime extension, or repowering. The last two options will increase the operating life and thus reduce lifecycle costs. These end-of-life decisions require careful consideration of the accumulated fatigue life of each turbine in a wind farm to minimize monetary risk for the wind farm operators. Today, this decision is primarily based on a single point assessment by the certification authority.
AIMWind proposes a continuous evaluation of wind farm health based on big data analytics using multimodal data such as wind, operational data, weather, condition monitoring, and inspection logs across a wind farm. Conventional approaches to fatigue estimation are slow and inadequate to achieve these goals, especially in large wind farms. Such a continuous health assessment will facilitate not only accurate life predictions but also continuous improvement of wind turbine operations to ensure long life and high availability.
Why is this important?
Wind turbines are certified for 20-25 years of design life. However, the sites for wind farms are typically leased for 40-50 years, while the structures and electrical installations are designed to last up to 50 years. This makes lifetime extension and repowering feasible options. The main outcome of AIMWind is to minimize the risks with these options, ensuring prolonged profitability. Besides, through the extension of condition monitoring, it is possible to achieve significant reductions in maintenance costs, which make up to 40% of lifecycle costs today. Furthermore, the technologies developed in this program, condition monitoring, data analytics for life assessment, and health-aware control systems are extendable to oil & gas, marine, and other technology-intensive industries. Extending the life of wind turbines also has a positive impact on the environment as it can significantly cut down their carbon footprint.
The objective is to develop essential technologies to ensure high profitability during design life and best remaining useful life beyond that period, improving prospects for lifetime extension and repowering.
A1. Extension of condition monitoring, diagnostics, and prognostics beyond a selected few components for accurate assessment of wind turbine and farm-level health assessment.
A2. Accurate turbine-level and farm-level fatigue assessment using artificial intelligence and data mining multimodal data sources such as weather, operational, condition monitoring, and maintenance data.
A3. Development of health-aware controls to achieve the dual objectives of life and efficiency in the wind farm operation.