人工智能技术在海上风机领域的应用综述

doi:10.19423/j.cnki.31-1561/u.2023.05.012

船舶 ›› 2023, Vol. 34 ›› Issue (05): 12-23.DOI: 10.19423/j.cnki.31-1561/u.2023.05.012

人工智能技术在海上风机领域的应用综述

康艺柔^1,2, 陈鹏^1,2, 程正顺^1,2, 胡志强^3,*

1.上海交通大学海洋工程国家重点实验室上海 200240;
2.上海交通大学三亚崖州湾深海科技研究院三亚 572024;
3.纽卡斯尔大学工程学院纽卡斯尔 NE1 7RU

收稿日期:2023-08-30 修回日期:2023-09-27 出版日期:2023-10-25 发布日期:2023-10-26
通讯作者: 胡志强（1975-）,男,博士,教授。研究方向：海洋可再生能源。
作者简介:康艺柔（1997-）,女,博士研究生。研究方向：海上风机技术。陈鹏（1993-）,男,博士,博士后。研究方向：海上风机技术、人工智能应用。程正顺（1987-）,男,博士,副教授。研究方向：海上风机技术、海洋结构物耦合动力分析与安全评估。胡志强,1998年上海交通大学获学士学位,2001年上海交通大学获硕士学位,2008年上海交通大学获博士学位。2001—2016年期间在上海交通大学工作,2016年至今任教于英国纽卡斯尔大学工程学院,教授（Lloyds Professor of Offshore Engineering）;担任船海技术系主任（Head of Marine, Offshore and Subsea Technology Group）、国际期刊《Ocean Engineering》副主编、International Ship Structures Congress（ISSC）V.6 Ocean Space Utilization委员,以及英国皇家造船学会（RINA）成员;研究领域包括: 浮式风机刚柔多体高阶耦合动力性能分析、海洋工程水动力学、船舶碰撞与搁浅及相关动力学。
基金资助:
国家自然科学基金项目（42176210; 52201330）

Review of Artificial Intelligence Technology Applications in Offshore Wind Turbines

KANG Yirou^1,2, CHEN Peng^1,2, CHENG Zhengshun^1,2, HU Zhiqiang^3,*

1. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
2. SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology, Sanya 572024, China;
3. School of Engineering, Newcastle University, Newcastle NE1 7RU, UK

Received:2023-08-30 Revised:2023-09-27 Online:2023-10-25 Published:2023-10-26

摘要/Abstract

摘要： 我国海上风机目前发展迅猛,人工智能技术在海上风机领域的应用是当前的研究热点。该文分析总结了多种人工智能技术在海上风机的动力响应分析、运维监测、设计运维优化等多个方向的应用,涉及动力响应预报、功率预报、结构监测、故障诊断、控制优化、运维优化等,分析表明人工智能技术可以较好地应对海上风机设计及运维领域的各项挑战。文中也论述了人工智能技术在海上风机领域的应用前景及目前存在的几种主要限制因素,并展望了未来发展方向。

关键词: 海上风机, 漂浮式风机, 人工智能, 深度学习, 深度强化学习

Abstract: At present, the offshore wind turbine in China is developing rapidly, and the application of artificial intelligence technology in the field of offshore wind turbine is the current research hotspot. This paper analyzes and summarizes the application of various artificial intelligence technologies in the dynamic response analysis, operation and maintenance monitoring, design, operation and maintenance optimization of offshore wind turbines in multiple directions, involving dynamic response forecasting, power forecasting, structural monitoring, fault diagnosis, control optimization, operation and maintenance optimization. The results show that AI technology can better cope with the challenges in the field of offshore wind turbine design and operation and maintenance. Finally, the application prospects of AI technology in the area of offshore wind turbines and several major constraints currently exist are discussed, and the future development direction is envisioned.

Key words: offshore wind turbine, floating wind turbine, artificial intelligence, deep learning, deep reinforcement learning

中图分类号:

TP18
TM614

康艺柔, 陈鹏, 程正顺, 胡志强. 人工智能技术在海上风机领域的应用综述[J]. 船舶, 2023, 34(05): 12-23.

KANG Yirou, CHEN Peng, CHENG Zhengshun, HU Zhiqiang. Review of Artificial Intelligence Technology Applications in Offshore Wind Turbines[J]. Ship & Boat, 2023, 34(05): 12-23.

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人工智能技术在海上风机领域的应用综述

Review of Artificial Intelligence Technology Applications in Offshore Wind Turbines

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