The quantity and size of wind farms continue to grow as countries around the world strive to meet ambitious targets for renewable electricity generation such as the UK governments Net Zero target of increasing offshore wind energy from current levels (circa 6 GW) to circa 75 GW by 2050. With increasing size and quantity of wind farms, there is a growing requirement to use wind farm level control both to help with grid integration and to minimise the loads on the turbines in the farm. In this paper, a methodology of distributing power set points through a wind farm to minimise the loads on the turbines whilst meeting a delta power set point for the farm is presented. The methodology in this paper uses a hierarchical control structure, in which a network wind farm controller calculates the required change in wind farm power and then passes this value on to a distributed controller that defines the change in power required from each wind turbine. The network wind farm controller calculates a delta change in wind farm power that the wind farm holds in reserve. The distributed controller allocates the reductions in power output by first setting a baseline reduction that considers the steady state tower loads. The baseline is then adjusted to meet the required change in power, distributing the additional change proportional to the square of each turbines estimated wind speed. Performance is assessed using the StrathFarm simulation tool. The wind turbine models incorporated into StrathFarm are sufficiently detailed to provide the tower, blade and drivetrain loads and the wind field model is sufficiently detailed to represent turbulence, wind shear and tower shadow and wakes and their interaction. The performance of the proposed wind farm controller is assessed for a range of wind conditions for a 4x4 wind farm of 5MW wind turbines. Both the accuracy of the change in power output from the wind farm and the change in turbines DELs are discussed. Depending on the wind conditions, the approach is found to reduce the tower and blade loads by about 10% in comparison to each turbine being allocated the same change in power. There is good accuracy in the change in power at higher wind speeds. Below rated wind speed, wake effects reduce the accuracy of the change in power.
|Number of pages||7|
|Publication status||Accepted/In press - 16 Jan 2020|
|Event||American Control Conference (ACC) 2020 - CO, USA, Denver, United States|
Duration: 1 Jul 2020 → 3 Jul 2020
|Conference||American Control Conference (ACC) 2020|
|Period||1/07/20 → 3/07/20|
- wind speed
- wind power
- renewable energy
Stock, A., Cole, M., Leithead, B., & Amos, L. (Accepted/In press). Distributed control of wind farm power set points to minimise fatigue loads. Paper presented at American Control Conference (ACC) 2020, Denver, United States.