Power train degradation modelling for multi-objective active power control of wind farms

Abstract

With the coming installation of hundreds of GW of offshore wind power, penetration of the inherent power fluctuations into the electricity grid will become significant. Therefore, the use of wind farms as power reserve providers to support the regulation of the grid’s voltage and frequency through delivering a desired power is expected to increase. As a result, wind turbines will not be necessarily delivering the maximum available power anymore – known as curtailed or derated operation – and will have to be able to deal with time-variant power demand. For this purpose, power setpoints from the grid are dispatched at the farm-level and then tracked at the turbine-level under the constraint of available power in the wind (known as active power control). The idea of this work is taking advantage of the additional degree of freedom lying in the power dispatch between turbines when operating in curtailed conditions. As failure of power train system components is frequent, costly and predictable, we seek to introduce power train degradation into the farm control objectives. To this end, a data-driven model of drivetrain fatigue damage as function of wind conditions and derating factor adapted to the farm active power control objective function is developed based on the pre-analysis of single-turbine simulations and degradation calculations, where the increased turbulence intensity due to wind farm wake effect is also considered. The proposed analytical power train degradation model is computationally efficient, can reflect the fatigue damage of individual gears and bearings in the overall power train life function and in contrast with high-fidelity models can be easily adjusted for different drivetrain configurations. A case study on the TotalControl reference wind power plant is demonstrated.