Ambition

Wind Energy is the cornerstone for the EU and the whole world to achieve clean growth and sustainable development. About 116 GW of new wind farms are expected to be installed in Europe from 2022-2026. However, wind farms’ operating efficiency is still severely constrained, and significant research & application gaps still exist, as follows:

• Needs of an innovative, scalable, practically applicable wind farm (WF) control system to fully understand the effects of aerodynamic interactions between turbines under time-varying environmental conditions.
• Needs of multi-objective handling abilities to mitigate different kinds of loads, achieve optimal fatigue distribution under different tasks, and ensure safe operation even under abnormal conditions.
• Needs of efficient digital and physical tools to significantly enhance data collection, data mining and virtual sensing, providing comprehensive information far beyond physical measurement level.
• Needs of comprehensive validations and demonstrations (e.g. dedicated experiments and real-world field tests) for new technologies. This should particularly consider the benefits and changes to lifetime management and LCOE.

ICONIC aims to fill all the above gaps, delivering disruptive technologies to achieve fundamental breakthroughs for wind farm O&M, enabling the transformation towards next-gen wind farm control approaches while meeting the core needs and requirements of wind farm owners, original equipment manufacturers (OEM), and critical component manufacturers to unleash the full potential of wind energy and maximise the economic & environmental benefits.

The core ambition of ICONIC is to deliver an innovative AI-based, data-powered, integrated wind farm control system. Here “integrated” means control strategies covering farm, turbine, and component levels while fully considering the influence of aerodynamic interactions between turbines on farm-wide power production. ICONIC’s control system will be able to not only maximise the farm power production and support the ancillary services for the main grid, but also mitigate the turbine and component loads and support the lifetime management and the decision-making of wind farms. This system will be hybrid – it will make full use of wind farm flow and wind turbine physics, and employ and develop state-of-the-art machine learning innovations to capture and cognise information that cannot be explicitly reflected by physics and measurements. It will be hierarchical – it will handle complex tasks via an easy-to-implement manner with minimised requirements in extensions and adjustments of current wind farm management systems. On the one hand, it will be fully functional with the mainstream wind farm/specifications without requiring new sensors/measurements. On the other hand, it will have interfaces (with plug-in plug-out features) for additional data from digital twins and new sensors to achieve control enhancement. All these designs will enable the results from ICONIC to be a paradigm in AI-enhanced wind energy operating systems in the near future.