Data-Driven Load Suppression and Platform Motion Optimization for Semi-Submersible Wind Turbines

To address the issues of large fatigue loads on key components and poor platform motion stability under the coupling effect of wind, waves, and internal excitations in semi-submersible wind turbines, this paper proposes a data-driven load suppression and platform motion optimization method. First, the NREL 5 MW OC4 semi-submersible wind turbine is used as the research object. Wind-wave environment and aeroelastic simulation models are constructed based on TurbSim and OpenFAST. The rainflow counting method and Palmgren–Miner rule are applied to calculate the damage equivalent load (DEL) of key components, and the platform’s maximum horizontal displacement (Smax) is defined to represent the motion range. Secondly, a systematic analysis is conducted to examine the effects of servo control variables such as generator speed, yaw angle, and active power on the DELs of the blade root, tower base, drivetrain, mooring cables, and platform Smax. It is found that the generator speed and the yaw angle have significant impacts, with the DELs of the blade root and drivetrain showing a strong positive correlation with Smax. On this basis, a fatigue load model based on random forests is established. A multi-objective optimization framework is built using the NSGA-II algorithm, with the objectives of minimizing the total DEL of key components and Smax, thereby optimizing the servo control parameters. Case studies based on actual marine environmental data from the East China Sea show that, compared to the baseline configuration (a typical unoptimized control strategy), the optimization results lead to a maximum reduction of 14.1% in the total DEL of key components and a maximum reduction of 16.95% in Smax. The study verifies the effectiveness of data-driven modeling and multi-objective optimization for coordinated control, providing technical support for improving the structural safety and operational stability of semi-submersible wind turbines.