Yaw misalignment in powertrain degradation modeling for wind farm control in curtailed conditions

Abstract

A framework characterizing the degradation of wind turbines for use in multiple-input damage-aware farm control is suggested. The focus is on the fatigue damage of the powertrain (drivetrain + generator) system, but the methodology may be extended to other components. A database of steady-state damage analyses for different operating conditions (average wind speeds, turbulence levels, power demands, and yaw misalignment angles) using aero-hydro-servo-elastic simulations is first generated. Then, a weighted damage index based on probabilistic long-term fatigue damage analysis of the powertrain system components is suggested and used to represent degradation at the farm level for control purposes. The focus is on curtailed conditions where the farm controller dispatches power commands to individual turbines in order to track a demanded power reference (rather than seeking to maximize power) at the farm level. As a secondary objective, the controller seeks to mitigate degradation through a smart combination of power commands and yaw offset angles, making use of the weighted degradation index. The potential of the proposed approach is demonstrated through a case study on the TotalControl Reference Wind Power Plant in a FLORIS-based simulation framework. The proposed farm controller is compared with the conventional one without damage mitigation feature and with damage mitigation but without yaw angle as the control input. It is found that combining yawing and downregulation effectively slows down degradation on the main bearing and powertrain as a whole.

Yaw misalignment in powertrain degradation modeling for wind farm control in curtailed conditions