数字化技术在深远海风电机组运维中的应用与展望

罗纯坤, 陈超, 陈孛, 巫发明, 华旭刚, 陈政清

太阳能学报 ›› 2026, Vol. 47 ›› Issue (8) : 646-666.

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (8) : 646-666. DOI: 10.19912/j.0254-0096.tynxb.2025-0510

数字化技术在深远海风电机组运维中的应用与展望

  • 罗纯坤1, 陈超1, 陈孛1, 巫发明2, 华旭刚1, 陈政清1
作者信息 +

APPLICATION AND PROSPECT OF DIGITAL TECHNOLOGIES IN OPERATION AND MAINTENANCE OF DEEP-SEA OFFSHORE WIND TURBINES

  • Luo Chunkun1, Chen Chao1, Chen Bei1, Wu Faming2, Hua Xugang1, Chen Zhengqing1
Author information +
文章历史 +

摘要

深远海风能开发作为新能源战略制高点,是突破近海资源瓶颈的必然选择,但面临恶劣海洋环境,风电机组的运维难度和成本激增。近年来,以人工智能、大数据、数字孪生为代表的数字化技术不断发展,为海上风力机实现智能化、自动化运维带来了新的机遇。首先,分析海上风力机的发展趋势:大容量与商业化、深远海与漂浮式、智能化与自动化;其次,归纳海上风力机检测与监测数据采集方法和设备以及先进数据分析方法,并对数字化技术在海上风力机各重要部件运维中的应用现状进行总结;最后,对漂浮式海上风力机智能化运维的未来研究重点进行展望。

Abstract

Deep-sea wind energy development represents a strategic high ground in renewable energy and an inevitable solution to overcoming near-shore resource constraints. However, harsh marine environments lead to significantly increasing difficulty and costs in wind turbine operation and maintenance (O&M). Recent advancements in digital technologies such as artificial intelligence, big data, and digital twins have created new opportunities for intelligent and automated O&M of offshore wind turbines. This paper first examines current development trends in offshore wind turbines: large capacity and commercialization, deep-sea and floating, intelligence and automation. Subsequently, it systematically reviews data acquisition methodologies and equipment for offshore wind turbines detection and monitoring, along with advanced data analysis techniques. The state-of-the-art applications of digital technologies in maintaining critical components of offshore wind turbines are comprehensively summarized. Finally, future research priorities for intelligent O&M of floating offshore wind turbines are prospected.

关键词

深远海风力机 / 漂浮式风力机 / 智能化运维 / 人工智能 / 大数据 / 数字孪生

Key words

deep-sea offshore wind turbines / floating wind turbines / intelligent operation and maintenance / artificial intelligence / big data / digital twin

引用本文

导出引用
罗纯坤, 陈超, 陈孛, 巫发明, 华旭刚, 陈政清. 数字化技术在深远海风电机组运维中的应用与展望[J]. 太阳能学报. 2026, 47(8): 646-666 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0510
Luo Chunkun, Chen Chao, Chen Bei, Wu Faming, Hua Xugang, Chen Zhengqing. APPLICATION AND PROSPECT OF DIGITAL TECHNOLOGIES IN OPERATION AND MAINTENANCE OF DEEP-SEA OFFSHORE WIND TURBINES[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 646-666 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0510
中图分类号: TK83   

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基金

国家自然科学基金联合基金(U24A20177); 国家杰出青年科学基金(52025082); 国家重点研发计划(2016YFE0127900); 湖南省“十大技术攻关项目”(2023GK1030)

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