Health Assessment and Early Warning of Offshore Wind Turbine Pitch Systems

FU Hao; YANG Yuan

doi:10.16516/j.ceec.2026-054

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Health Assessment and Early Warning of Offshore Wind Turbine Pitch Systems

更新时间：2026-10-14

- Health Assessment and Early Warning of Offshore Wind Turbine Pitch Systems
- Southern Energy Construction Vol. 13, Issue 3, Pages: 187-196(2026)
- 作者机构：
  
  中国能源建设集团广东省电力设计研究院有限公司,广东,广州,510663
- 作者简介：
- 基金信息：
- DOI：10.16516/j.ceec.2026-054
  CLC： TK89;TK83
- Published：2026
- 稿件说明：
移动端阅览
付豪, 杨源. 海上风电变桨系统健康度诊断与预警[J]. 南方能源建设, 2026, 13(3): 187-196.

.
付豪, 杨源. 海上风电变桨系统健康度诊断与预警[J]. 南方能源建设, 2026, 13(3): 187-196. DOI： 10.16516/j.ceec.2026-054.

. DOI： 10.16516/j.ceec.2026-054.

摘要

目的

随着海上风电机组装机规模越来越大，且不断向深远海迈进，海上风电机组的故障预警和检修面临着严峻的挑战。变桨系统是海上风电机组中故障率最高的部件之一，传统的故障预警模式已不能满足机组的需求。因此，有必要对海上风电机组进行健康度诊断和故障预警。

方法

文章梳理了变桨系统整个工艺流程上可能出现故障的环节及其故障的主要表现形式、故障特征的变化规律，为变桨系统健康度评估模型的建立提供机理性保证及可解释性。在机理分析的基础上，应用数据分析、机器学习算法及马氏距离计算等手段，实时展现变桨系统的健康度。

结果

通过健康度诊断和滑动窗口技术有效区分轻型故障和严重故障，并根据轻型故障出现的概率，给出相应的健康度预警和维修建议。

结论

通过提前预警和提前检修，提高了设备的可靠性，降低了重大故障的发生概率，同时也节省了停机时间及运维成本。

Abstract

Objective

With the increasing scale of offshore wind turbine installations and the continuous expansion into deeper waters

fault warning and maintenance of offshore wind turbines face significant challenges. The pitch system is one of the components with the highest failure rates in offshore wind turbines

and traditional fault prediction methods can no longer meet the operational requirements. Therefore

it is essential to implement health assessment and fault warning for offshore wind turbines.

Method

This paper systematically reviewed potential failure points throughout the enti

re process of the pitch system

along with their primary manifestations and the evolution patterns of fault characteristics

providing a mechanistic foundation and interpretability for the development of a health assessment model for the pitch system. Based on mechanism analysis

data analysis

machine learning algorithms and Mahalanobis distance calculation were applied to present the health status of the pitch system in real time.

Result

By leveraging health assessment and sliding window techniques

light faults and severe faults are effectively distinguished. Additionally

corresponding health warnings and maintenance recommendations are provided based on the probability of light faults occurring.

Conclusion

Through early warning and proactive maintenance

the reliability of equipment is improved

major failures probability are reduced and downtime and operation and maintenance costs are saved at the same time.

关键词

Keywords

references

ZHU J Z, GAO M Y, XIAO P F, et al. Review of grid integration and operation technologies for large-scale offshore wind power [J]. Electric power engineering technology, 2025, 44(5): 2-24. DOI: 10.12158/j.2096-3203.2025.05.001.

TAO S Y, JIANG F Q. Review of the key technologies of wind farm cluster prediction, planning and control [J]. Electric power engineering technology, 2024, 43(1): 86-99. DOI: 10.12158/j.2096-3203.2024.01.010.

LI Z C, LI X H, LAO J S, et al. Study on the siting of offshore wind farms and prospects for future development [J]. Southern energy construction, 2025, 12(1): 127-140. DOI: 10.16516/j.ceec.2024-147.

CWEA. Statistical briefing on China's wind power lifting capacity in 2025 [R]. CWEA, 2026.

XU T, LIU Z N, LU Y N, et al. Key technologies and applications of multi-source collaborative optimization control for stability of receiving-end power grid [J]. Power system and clean energy, 2025, 41(6): 115-123. DOI: 10.16516/j.gedi.issn2095-8676.2023.04.010.

MA S D, GONG W B, WAN A P. Fault diagnosis of main bearings in offshore wind turbines based on transfer learning [J]. Power systems and big data, 2024, 27(4): 16-24. DOI: 10.19317/j.cnki.1008-083x.2024.04.002

XING Y. Key technologies for operational condition monitoring and health maintenance of wind turbines [D]. Beijing: North China Electric Power University, 2021. DOI: 10.27140/d.cnki.ghbbu.2021.000150.

ZOU X M, WANG C G, HU Y J. Physical rotor speed recovery strategy for grid-forming permanent magnet synchronous generator-based wind turbines under turbulent wind conditions [J]. Electric power engineering technology, 2025, 44(5): 100-108. DOI: 10.12158/j.2096-3203.2025.05.009.

ZHANG X L, SUN A C, HAO S P, et al. Multi-machine cooperative control strategy of wind farm participating in power grid frequency modulation [J]. Power generation technology, 2024, 45(3): 448-457. DOI: 10.12096/j.2096-4528.pgt.23068.

ZHANG Y, LI F T, ZHANG G H, et al. Optimization and scheduling methods for wind power high-penetration power systems considering wind power ramping reserve requirements [J]. Power system protection and control, 2024, 52(23): 95-106. DOI: 10.19783/j.cnki.pspc.240373.

JIA Z W. Research on operational condition monitoring and health maintenance system for wind turbine [D]. Beijing: North China Electric Power University, 2020. DOI: 10.27140/d.cnki.ghbbu.2020.000074.

LUO K, GUO J B, MA S C, et al. Review of key technologies of reliability analysis and improvement for offshore wind power grid integration [J]. Power system technology, 2022, 46(10): 3691-3702. DOI: 10.13335/j.1000-3673.pst.2022.1496.

HUANG L L, MA Y J, YING F X, et al. Dynamic group maintenance strategy for a wind farm based on residual life prediction information [J]. Power system protection and control, 2024, 52(16): 178-187. DOI: 10.19783/j.cnki.pspc.231397.

HE B. Research on health status assessment and prediction of wind turbine units based on data mining [D]. Guiyang: Guizhou University, 2023. DOI: 10.27047/d.cnki.ggudu.2023.001784.

RAO L, HE X, WU Q, et al. Health status assessment method of pitch system of offshore wind turbine based on improved DS evidence theory [J]. Ship engineering, 2022, 44(Suppl. 2): 20-26. DOI: 10.13788/j.cnki.cbgc.2022.S2.05.

YIN Z K, LIN Z W, LV G H, et al. Research on fault diagnosis and health state prediction of wind turbine variable pitch system based on data drive [J]. Journal of Northeast Electric Power University, 2023, 43(5): 1-11, 17. DOI: 10.19718/j.issn.1005-2992.2023-05-0001-12.

WANG W D. Analysis of overspeed caused by abnormal electric pitch system of wind turbine [J]. Electrical automation, 2025, 47(2): 112-115,118. DOI: 10.3969/j.issn.1000-3886.2025.02.030.

ZHANG F X, MING L. Common faults and optimization measures of propeller-changing system in wind farm fans [J]. Sci-tech innovation productivity, 2025, 46(3): 74-77. DOI: 10.3969/j.issn.1674-9146.2025.03.074.

LI S, LI P, WANG Z G. Exploration of emergency response technology for backup power supply failure of fan pitch system [J]. Electronic components and information technology, 2025, 9(5): 224-227. DOI: 10.19772/j.cnki.2096-4455.2025.05.069.

LUO Y, HAO W Q. A method for wind turbine fault diagnosis of CNN based on SMOTE [J]. Computer simulation, 2024, 41(2): 91-94, 353. DOI: 10.3969/j.issn.1006-9348.2024.02.017.

YAO J J. Few-shot fault diagnosis for pitch system of wind turbines based on prototypical network with Mahalanobis distance [D]. Shenyang: Shenyang University of Technology, 2022. DOI: 10.27322/d.cnki.gsgyu.2022.000236.

QIU J M. Research on online fault early warning and intelligent diagnosis system of wind energy capture system of wind turbine [D]. Beijing: North China Electric Power University, 2020. DOI: 10.27140/d.cnki.ghbbu.2020.000598.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Comprehensive Application and Progress of Energy Storage Technologies on the Generation Side

Review of Safety Risk Early Warning Technology and Engineering Application for Lithium Battery Energy Storage Power Stations

Advances in Numerical Simulation for Deep Underground Energy Storage and Compressed Air Energy Storage: A Review

Compressed Air Supply System Based on Binary Gas Compression Energy Storage

Multi-Time Scale Inertia Coordination and Transient Stability Optimization Control of High Proportion Wind-Solar Cluster Grid-Connected

Related Author

ZHOU Yiming

LI Xiaoyong

CHEN Xiaoqing

Lin ZHOU

Wei SHI

✉

Songhao ZHANG

Hengjun LI

Related Institution

Jiangsu Goldwind Science & Technology Corporation

China International Engineering Consulting Corporation

Deepwater Engineering Research Center， Dalian University of Technology

State Key Laboratory of Coastal and Offshore Engineering， Dalian University of Technology

State Key Laboratory of Hydraulic Engineering Simulation and Safety， Tianjin University

AI问答

⁰