D4REL – Design for Reliable Power Performance

Publieke samenvatting / Public summary

Design for Reliable Power Performance (D4REL) is an R&D project aiming at developing innovative technology & tools for reducing uncertainty in both the design and operation of offshore wind farms, with the goal of achieving substantial reduction of 6.4% of the cost of energy (CoE). This project aims to develop new technology and tools for the reduction of uncertainty in both the design phase of wind farms and its operational life. By lowering design and operational uncertainty, the designs can be made less conservative and operation can be optimised.

Improving the predictability of the performance of large offshore wind farms implies the development of tools and methods that assist the designer and operator/developer in achieving a reliable asset management of the offshore wind power plant. Key in this target is the optimal operation of the wind turbine and the ability of the wind turbines to take the autonomous decisions on optimal operation.

This project has developed a wide range of novel technologies that increase the reliability of the wind turbine and reduce the performance uncertainties. These are:

  • Availability improvement in electrical generator systems by means of:
    Modular concepts in power electronic converters: these are shown to reduce the failure rates and increase the availability of generator systems
    Converter topologies: different three-level topologies, the 3L-ANPC and the 3L-T2C show the highest lifetimes
    Dynamic thermal management: control of junction temperature is shown to achieve a significant reduction of damage in the power semiconductor.
  • Improved knowledge and design capability (modelling) enabling the development of the next generation of larger and lighter offshore wind turbine blades:
    • Vortex generators: these are blade add-ons that increase the power yield significantly, and enable the use of thicker airfoil profiles
    • Thick trailing edges: they enable the use of long slender rotors equipped with thicker blades, enabling the design of larger rotors with smaller solidity.
    • Modelling of thick airfoils: Improved lift and drag prediction enables more reliable design (low uncertainty) design of the next generation thicker blades
  • Probabilistic support structure design:
    • By taking the uncertainties in the support structure design explicitly (instead of indirect by using partial safety factors), design of support structure with a lower mass is shown viable.
  • Wind turbine modelling and control:
    • System identification: improved nonlinear system identification methods are developed, including uncertainty quantification, to enable the use of advance adaptive control algorithms
    • An adaptive control algorithm is developed, with its parameters being adapted online during operation based on the measured/identified support structure parameters. It reduces the loads on the support structure in the presence of uncertainty in the support structure frequency due to design uncertainties (manufacturing, installation, soil) and operational uncertainties (scour, formation of marine sand dunes and biofouling). It also enables significant material cost reduction in the tower.
    • Condition-based control: the controller is adjusted to reduce the loads on a part with a deteriorating condition, with the aim of delaying failure and increasing the total energy produced until the moment of failure. This reduces downtime substantially.

These results have been disseminated over various journals, conferences and workshops.

The impact of the project is estimated as:

  • AEP improvement: approximately is 2.5%, based on 2% increase in AEP due to vortex generators and approximately 0.5% increase in availability.
  • 9.7% reduction of the support structure CAPEX. The share of the support structure cost on the total required CAPEX of an offshore wind farm is approximately 20 – 25%, for water depth of approximately 30 – 35 m. This would then result in a total reduction of CAPEX of approximately 2%.

This boils down to 3.6% cost reduction of the LCoE.