海上风电变桨系统健康度诊断与预警

付豪; 杨源

doi:10.16516/j.ceec.2026-054

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海上风电变桨系统健康度诊断与预警

海上风电技术 | 更新时间：2026-10-14

- 海上风电变桨系统健康度诊断与预警
- Health Assessment and Early Warning of Offshore Wind Turbine Pitch Systems
- 南方能源建设 2026年13卷第3期页码：187-196
- 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：
  
  广东省新型能源体系产业科技互促双强重点工程实施计划“深远海60 m级海上风电支撑结构体系优化技术”（ER13261W）
- DOI：10.16516/j.ceec.2026-054
  中图分类号： TK89;TK83
- 纸质出版：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

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