Popular science Presentation
Generally, wind turbines are designed to operate for 20-25 years. The stipulation assumes that the wind turbine will experience wind loads high enough to cause significant wear through this time that is evenly distributed over all turbines. Research shows that the wear over time can widely vary since wind is an uncertain source of energy and thus loading is unevenly distributed over the turbines. Therefore, not all turbines in a large wind farm will reach end-of-life after 25 years of operation. Thousands of wind turbines in Europe reach this stipulated lifetime in the coming decades. Which of these turbines have remaining useful life? Can we continue operating those turbines safely? These are the questions AIMWind will strive to answer.
AIMWind will equip wind turbines with new technologies that assess wear and ageing, and adapt windfarms operations to bring holistic improvements to their longevity and profitability during their design life and beyond.
The AIMWind project will take a three-pronged approach towards improving the profitability and life of wind farms: i) advanced condition monitoring and prognostics techniques will be developed to detect early faults and determine the remaining useful life, rigorously and continuously; ii) the power of big data and data analytics will be leveraged to combine multiple sources of information such as the operations, condition monitoring data, weather conditions and inspection data to identify the health status of the wind turbine and windfarm; iii) novel control methodologies will be developed that take into account the present health status and adapt the operations to achieve dual objectives of sustained health and efficiency.
The project is a collaborative research effort from University of Agder, Norwegian Research Centre (NORCE) and Delft University of Technology (TU Delft), The Netherlands.
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.
The project AIMWind will take a three-pronged approach. 1) We will also extend the condition monitoring systems as existing systems today focus only on a selected set of components providing incomplete health information. 2) We will develop big data analytics using a fusion of physics-based models and novel deep-learning techniques to adequately estimate the accumulated fatigue in real-time, which does not exist today. We will use NORCOWE wind measurements, reference wind farm data, and other open data sources to achieve this (more details in the proposal document). 3) We develop health-aware control technologies to achieve the dual objectives of efficiency and long life.
Thus, AIMWind plans to build the essential knowledge to reliable and efficient wind farm operation and improved chances for lifetime extension and repowering.