基于长短期记忆网络的大型漂浮式风力发电机平台运动极短期预报方法
收稿日期: 2023-07-02
修回日期: 2023-07-23
录用日期: 2023-08-21
网络出版日期: 2023-11-10
基金资助
国家自然科学基金(42176210);国家自然科学基金(52201330)
Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM Network
Received date: 2023-07-02
Revised date: 2023-07-23
Accepted date: 2023-08-21
Online published: 2023-11-10
大型漂浮式风力发电机平台运动响应的超前预报是实现主动调载系统控制和智慧运维监测的关键技术.然而,漂浮式风力发电机复杂的工作环境使得仅依靠物理模型和数值仿真方法的极短期预报具有非常大的挑战.因此,提出一种创新的基于长短期记忆神经网络的漂浮式风力发电机平台运动极短期预报方法,并利用实测数据开展了浮式平台纵荡运动的验证与不确定性分析.结果表明,该极短期预报方法可以获得较好的精度,超前60 s预报工作状态下纵荡运动的均方误差最大仅约为1%.该大型漂浮式风力发电机极短期运动响应预报能够为未来漂浮式风电场的智慧运维提供扎实的技术支撑.
关键词： 大型漂浮式风力发电机; 极短期预报; 长短期记忆网络; 不确定性
卫慧, 陈鹏, 张芮菡, 程正顺 . 基于长短期记忆网络的大型漂浮式风力发电机平台运动极短期预报方法[J]. 上海交通大学学报, 2023 , 57(S1) : 37 -45 . DOI: 10.16183/j.cnki.jsjtu.2023.S1.27
The motion prediction of large floating wind turbine platforms is the key technology to realize the control of active ballast systems and intelligent operation and maintenance monitoring. However, the complex environment of floating wind turbines makes ultra-short-term predictions that only rely on physical models and numerical simulation methods very challenging. Therefore, this paper proposes an innovative ultra-short-term prediction method for floating wind turbine platform motion based on the long-short-term memory (LSTM) neural network. Measured data have been used to verify the feasibility and uncertainty of this method in terms of surge motion. The results show that the ultra-short-term prediction method proposed in this paper can obtain a better accuracy. For example, the maximum mean square error of surge motion prediction in the 60 s under working condition is only about 1%. The ultra-short-term motion prediction of large floating wind turbines proposed in this paper provides solid technical support for future intelligent operation and maintenance of floating wind farms.
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