Optimization of wind‐wave hybrid system based on wind‐wave coupling model

• Published in: IET renewable power generation 2024-06, Vol.18 (8), p.1407-1427
• Main Authors: Liu, Tiesheng, Liu, Yanjun, Huang, Shuting, Xue, Gang
• Format: Article
• Language: English
• Subjects: Air-turbines; Alternative energy sources; Analysis; Electric power production; hybrid renewable energy systems; Neural networks; radial basis function networks; wave power generation; wind turbines
• ISSN: 1752-1416; 1752-1424
• DOI: 10.1049/rpg2.12980

The integration of wave energy converters (WECs) into floating offshore wind turbine (FOWT) can effectively reduce costs and increase power generation. When WECs are integrated into FOWT, the hydrodynamic interference, motion coupling, and other factors contribute to the high spatial dimension of the coupled optimization, making it difficult to find the globally optimal solution. Therefore, this study proposes an optimization method based on a wind‐wave coupling model, and takes a new wind‐wave hybrid system as an example for verification and analysis. First, the experimental design is completed through random sampling, and the corresponding WECs and wind turbine power of each sample point are calculated using full coupling simulation. And then according to the design input and simulation results, the wind‐wave coupling model is obtained by training the elliptical basis functions neural network (EBFNN). At last, based on this model, the non‐dominated sorting genetic algorithm II (NSGA‐II) is used to optimize the WECs microarray. The results show that the prediction model established in this paper has high accuracy and is used with the NSGA‐II to effectively improve the wind‐wave coupling energy harvesting. This method can effectively solve the problem of high coupling dimensions in the process of hybrid system design. This study proposes an optimization method based on a wind‐wave coupling model, and takes a new wind‐wave hybrid system as an example for verification and analysis. The results show that the non‐parametric prediction model established in this paper has high accuracy and is used with the NSGA‐II optimization algorithm to effectively improve the wind‐wave coupling energy harvesting. This method can effectively solve the coupling problem of motion and force among wind turbine, platform, and wave energy converters in the design process of a wind‐wave hybrid system.