Project

The project aims to develop wind farm models based on data and artificial intelligence algorithms. The model and data will support the design of intelligent control algorithms for wind farms. This modeling method is used to solve the problem that existing models cannot be used for actual wind farm control. The model uses machine learning models to learn high-fidelity model data to improve the performance of low-fidelity models. So as to achieve the balance between the fidelity required by the control algorithms and the computational cost. The wind farm control algorithm based on this model aims to improve the power production and turbine life of the total farm by intelligent wake redirection. The wind power industry will also benefit from the development of artificial intelligence algorithms. Reinforcement learning is used to design intelligently optimized controllers for wind farms.

Description
100 kW EXOWAVE wave energy testing in Hanstholm.

Key results
• Design, build and demonstrate an Exowave wave energy converter (WEC) block at a 14-meter water depth in the Danish North Sea in conjunction with a hydro turbine driven electrical generator connected to the grid. The power generation would be +100 kW.
• Include learnings from EUDP1: numerical model verified by tank test (AAU) and CFD analysis (Delft University), feasibility study: wind and wave plant in very large scale, WEC detailed design and engineering, FAT and demonstration at DanWEC site.
• Assess the environmental impact and improve animal life by shaping the WEC foundation for fish breeding grounds.
• Life cycle analysis and include eco-friendly materials as waste materials from wind turbine blade waste materials.
• Assess supply chain in the North Sea region with special focus in Denmark and its raw material, production facilities, knowledge provider for fulfilling the aim above LOI target and support the Danish national energy target in 2030 and 2050. And to include the results in the design phase. The overall KPI here is to lower LCOE.
• TRL improve from 6 to 7

Moorings of floating oil and gas (O&G) structures present surprisingly large failure rates. A top solution is a redundancy in the design. However, marine renewables cannot afford such redundancy in the mooring design to obtain a competitive levelised cost of energy (LCOE). The EU-funded ISLINGTON project will reduce uncertainties in the estimated fatigue damage of mooring cables due to soil-cable interaction in the touch-down zone (TDZ) and the economic cost for marine renewables. ISLINGTON will improve the numerical modelling of the cable-soil interaction in the TDZ for mooring cables, generate experimental data for mooring line trenching and perform a numerical investigation of the effect of trenching on the fatigue of mooring cables.