基于PINN的漂浮式风力机响应预测与参数反演研究

杨新梦; 何仑; 张锐星; 黄增浩; 安利强

doi:10.19912/j.0254-0096.tynxb.2025-0401

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (7) : 181-190. DOI: 10.19912/j.0254-0096.tynxb.2025-0401

基于PINN的漂浮式风力机响应预测与参数反演研究

杨新梦¹, 何仑¹, 张锐星¹, 黄增浩^1,2, 安利强¹

作者信息 +

RESEARCH ON RAPID RESPONSE PREDICTION AND PARAMETER INVERSION OF SEMI-SUBMERSIBLE WIND TURBINES BASED ON PINN

Yang Xinmeng¹, He Lun¹, Zhang Ruixing¹, Huang Zenghao^1,2, An Liqiang¹

Author information +

文章历史 +

摘要

面向漂浮式风力机响应快速预测及参数反演的需求,提出基于物理信息神经网络（PINN）的漂浮式风力机动力学无网格求解方法。将漂浮式风力机六自由度动力学方程嵌入损失函数,进行空间域优化,实现其动力学响应的高效预测与参数反演。结果表明,相较于传统数值计算方法,PINN在保证高精度的同时大幅提升了计算效率,并有效解决了大步长条件下的误差累积问题。此外,采用逆PINN（IPINN）架构进行系统参数反演,误差可控制在1%以内,展示了精准识别系统参数的能力。

Abstract

Aiming to address the demands for rapid response prediction and parameter inversion of floating wind turbines, this study proposes a mesh-free solution method based on physics-informed neural network（PINN） for floating wind turbine dynamics. By embedding the six-degree-of-freedom dynamic equations of the floating wind turbines into the loss function and optimizing the spatial domain, the method achieves efficient prediction of dynamic responses and parameter inversion. The results demonstrate that, compared to traditional numerical methods, PINN significantly enhances computational efficiency while maintaining high accuracy, effectively overcoming error accumulation under large time-step conditions. Furthermore, the inverse PINN （IPINN） architecture achieves system parameter inversion with errors controlled within 1%, demonstrating precise identification capabilities.

导出引用

杨新梦, 何仑, 张锐星, 黄增浩, 安利强. 基于PINN的漂浮式风力机响应预测与参数反演研究[J]. 太阳能学报. 2026, 47(7): 181-190 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0401

Yang Xinmeng, He Lun, Zhang Ruixing, Huang Zenghao, An Liqiang. RESEARCH ON RAPID RESPONSE PREDICTION AND PARAMETER INVERSION OF SEMI-SUBMERSIBLE WIND TURBINES BASED ON PINN[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 181-190 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0401

中图分类号： TM315

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