Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.

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.

Offshore wind power currently shows the trend of a larger wind turbine capacity and deep-sea wind farm sites. Traditional wind farms with single-type wind turbines can hardly accommodate the dual requirements of high-efficiency power generation and wake effect mitigation of wind farms. Moreover, the wind shear of fixed wind turbines and the platform motion of floating wind turbines result in insufficient adaptability to a hybrid wind farm with multi-type wind turbines. To address that issue, this paper takes offshore wind farms with a multi-type hybrid layout for wake optimization. Firstly, based on the wind shear model, the influence of hub height difference for fixed wind turbines is analyzed, and the platform motion of semi-submersible floating wind turbines is evaluated through MoorDyn. On this basis, the wake optimization strategy for maximizing the total power generation of a wind farm is proposed based on the Gaussian Curl Hybrid model, which realizes three-dimensional wake control by considering the hub height difference and floating platform motion of multi-type wind turbines. The case study demonstrates that the multi-type hybrid layout itself has inherent wake suppression and optimization potential. The fixed wind farm with a row–column hybrid layout achieves an average power generation efficiency of 65.25%, which is superior to the single-type layout. For the floating wind farm with an inner–outer hybrid layout, the displacement misalignment effect is significant, with a maximum offset of 21.66 m in surge and 10.32 m in sway, and the total power is increased by 6.87 MW. And a hierarchical wake control mode matching multi-type wind turbines is formed. It provides a novel wake regulation mechanism for the design and operation of hybrid offshore wind farms.