Condition Monitoring of Wind Turbine Gearbox Based on Improved Feature Crossover Algorithm

LIU Changliang; TIAN Wenwen; ZHANG Shuyao; YIN Lei; LIU Shuai

doi:10.19805/j.cnki.jcspe.2025.240346

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Condition Monitoring of Wind Turbine Gearbox Based on Improved Feature Crossover Algorithm

更新时间：2026-10-10

- Condition Monitoring of Wind Turbine Gearbox Based on Improved Feature Crossover Algorithm
- Chinese Journal of Power Engineering Vol. 45, Issue 7, Pages: 1072-1081(2025)
- 作者机构：
  
  1. 华北电力大学控制与计算机工程学院,北京,102206
  2. 华北电力大学保定市综合能源系统状态检测与优化调控重点实验室,河北,保定,071003
  3. 华北电力大学自动化系,河北,保定,071003
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240346
  CLC：
- Online First：14 July 2025，
  
  Published：2025
- 稿件说明：
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刘长良,田雯雯,张书瑶,尹磊,刘帅. 基于改进特征交叉算法的风电机组齿轮箱状态监测动力工程学报, 2025, 45(7): 1072-1081 https://doi.

org/10.19805/j.cnki.jcspe.2025.240346
刘长良,田雯雯,张书瑶,尹磊,刘帅. 基于改进特征交叉算法的风电机组齿轮箱状态监测动力工程学报, 2025, 45(7): 1072-1081 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240346.

org/10.19805/j.cnki.jcspe.2025.240346 DOI：

摘要

针对风电机组监控与数据采集(SCADA)数据间存在的非线性关联问题

引入特征交叉机制并进行改进

将其应用于风电机组齿轮箱状态监测领域。首先

提出一种两阶段交叉特征选择方法

该方法综合考虑了变量间的因果性、相关性及数据分布差异

以筛选具有强隐藏关联且低冗余度的特征进行交叉;其次

对因子分解机进行改进

仅将交叉特征组内的基准变量与其余变量进行交叉

在生成合理交叉特征的同时显著缩短了生成时间;最后

将改进特征交叉算法用于某风电场齿轮箱状态监测任务中。结果表明:所提方法与五种模型相结合均能取得优异效果

显著提升了模型监测性能。

Abstract

To address the issue of non-linear correlation between SCADA data of wind turbine units

a feature crossover mechanism was introduced and improved

and used for monitoring status of wind turbine gearboxes. Firstly

a two-stage cross feature selection method was proposed

which comprehensively considered the causality

correlation and data distribution differences among variables to screen features with strong hidden associations and low redundancy for cross-selection. Secondly

the factorization machine was improved by crossing only the benchmark variables within the cross feature group with the remaining variables

significantly shortened the generation time while generating reasonable cross features. Finally

the improved feature crossover algorithm was applied to the condition monitoring task of gearboxes in a certain wind farm. The results show that the proposed method

combined with five models

can achieve excellent effects and significantly improve the models' monitoring performance.

关键词

Keywords

references

胡阳, 胡耀宗, 程逸, 等. 基于FD-AT-LSTM的大型风电机组变频器温度状态监测[J]. 动力工程学报, 2023, 43(9): 1207-1215. HU Yang, HU Yaozong, CHENG Yi, et al. Temperature monitoring of heavy wind power unit inverters based on FD-AT-LSTM[J]. Journal of Chinese Society of Power Engineering, 2023, 43(9): 1207-1215.

风能北京宣言开发30亿风电, 引领绿色发展, 落实“3060”目标[J]. 风能, 2020(11): 34-35. Beijing declaration on wind energy develop 3 billion wind power, lead green development and implement the '3060' goal[J]. Wind Energy, 2020(11): 34-35.

罗志宏, 刘长良, 刘帅. 基于近邻元分析的风电机组状态监测特征选择方法[J]. 华北电力大学学报(自然科学版), 2024, 51(3): 134-142. LUO Zhihong, LIU Changliang, LIU Shuai. Feature selection method for wind turbine condition monitoring based on neighborhood component analysis[J]. Journal of North China Electric Power University (Natural Science Edition), 2024, 51(3): 134-142.

TAUTZ-WEINERT J, WATSON S J. Using SCADA data for wind turbine condition monitoring-a review[J]. IET Renewable Power Generation, 2017, 11(4): 382-394.

郑毅, 王承民, 刘保良, 等. 基于多层级时空图神经网络的风电机组在线异常检测[J]. 电力系统自动化, 2024, 48(5): 107-119. ZHENG Yi, WANG Chengmin, LIU Baoliang, et al. Online anomaly detection of wind turbines based on hierarchical spatio-temporal graph neural network[J]. Automation of Electric Power Systems, 2024, 48(5): 107-119.

WU Zhenyu, LI Yanting, WANG Peng. A hierarchical modeling strategy for condition monitoring and fault diagnosis of wind turbine using SCADA data[J]. Measurement, 2024, 227: 114325.

YANG Chunhua, ZHANG Jiaojiao, WU Dehao, et al. Variable partition based parallel dictionary learning for linearity and nonlinearity coexisting dynamic process monitoring[J]. Control Engineering Practice, 2024, 142: 105750.

景彤梅, 齐咏生, 刘利强, 等. 基于KECA-GRNN的风电机组齿轮箱状态监测与健康评估[J]. 太阳能学报, 2021, 42(6): 400-408. JING Tongmei, QI Yongsheng, LIU Liqiang, et al. Condition monitoring and health assessment of wind turbine gearbox based on KECA-GRNN[J]. Acta Energiae Solaris Sinica, 2021, 42(6): 400-408.

刘家瑞, 杨国田, 杨锡运. 基于深度卷积自编码器的风电机组故障预警方法研究[J]. 太阳能学报, 2022, 43(11): 215-223. LIU Jiarui, YANG Guotian, YANG Xiyun. Research on wind turbine fault warning method based on deep convolution auto-encoder[J]. Acta Energiae Solaris Sinica, 2022, 43(11): 215-223.

WANG Kai, YAN Caoyin, MO Yanfang, et al. Anomaly detection using large-scale multimode industrial data: an integration method of nonstationary kernel and autoencoder[J]. Engineering Applications of Artificial Intelligence, 2024, 131: 107839.

陆悦聪, 王瑞琴, 金楠. 基于多维特征交叉的深度协同过滤算法[J]. 计算机工程与应用, 2022, 58(22): 72-78. LU Yuecong, WANG Ruiqin, JIN Nan. Deep collaborative filtering algorithm based on multi-dimensional feature crossover[J]. Computer Engineering and Applications, 2022, 58(22): 72-78.

王越, 于莲芝. 一个以注意力机制结合隐式和显式的特征交叉的CTR预估模型[J]. 小型微型计算机系统, 2021, 42(9): 1884-1890. WANG Yue, YU Lianzhi. CTR Prediction model combining implicit and explicit features with attention mechanism[J]. Journal of Chinese Computer Systems, 2021, 42(9): 1884-1890.

刘雨佳, 樊艳芳, 白雪岩, 等. 基于特征交叉机制和误差补偿的风力发电功率短期预测[J]. 电工技术学报, 2023, 38(12): 3277-3288. LIU Yujia, FAN Yanfang, BAI Xueyan, et al. Short-term wind power prediction based on feature crossover mechanism and error compensation[J]. Transactions of China Electrotechnical Society, 2023, 38(12): 3277-3288.

于旭, 何亚东, 杜军威, 等. 一种结合显式特征和隐式特征的开发者混合推荐算法[J]. 软件学报, 2022, 33(5): 1635-1651. YU Xu, HE Yadong, DU Junwei, et al. Developer hybrid recommendation algorithm based on combination of explicit features and implicit features[J]. Journal of Software, 2022, 33(5): 1635-1651.

李兴兵, 谢珺, 续欣莹, 等. 基于改进FM算法和注意力机制的深度点击率预估模型[J]. 南京理工大学学报, 2021, 45(4): 429-438. LI Xingbing, XIE Jun, XU Xinying, et al. Deep click rate prediction model based on improved FM algorithm and attention mechanism[J]. Journal of Nanjing University of Science and Technology, 2021, 45(4): 429-438.

张可, 张政, 金伟. 数据-知识融合的水利工程建设安全风险灰色因子分解机预测模型[J]. 水利水电技术(中英文), 2024, 55(1): 134-143. ZHANG Ke, ZHANG Zheng, JIN Wei. Data and knowledge-driven grey factorization machine prediction model for safety risk in water conservancy engineering construction[J]. Water Resources and Hydropower Engineering, 2024, 55(1): 134-143.

高明裕, 蔡林辉, 孙长城, 等. 一种基于斯皮尔曼秩相关结合神经网络的电池组内部短路故障检测算法[J]. 电子与信息学报, 2022, 44(11): 3734-3747. GAO Mingyu, CAI Linhui, SUN Changcheng, et al. An internal short circuit fault detecting of battery pack based on spearman rank correlation combined with neural network[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3734-3747.

张书瑶, 王梓齐, 刘长良. 基于改进集成KNN回归算法的风电机组齿轮箱状态监测[J]. 动力工程学报, 2023, 43(6): 759-767. ZHANG Shuyao, WANG Ziqi, LIU Changliang. Wind turbine gearbox condition monitoring based on improved ensemble KNN regression algorithm[J]. Journal of Chinese Society of Power Engineering, 2023, 43(6): 759-767.

YU Wanke, ZHAO Chunhui, HUANG Biao. MoniNet with concurrent analytics of temporal and spatial information for fault detection in industrial processes[J]. IEEE Transactions on Cybernetics, 2022, 52(8): 8340-8351.

刘纪平, 梁恩婕, 徐胜华, 等. 顾及样本优化选择的多核支持向量机滑坡灾害易发性分析评价[J]. 测绘学报, 2022, 51(10): 2034-2045. LIU Jiping, LIANG Enjie, XU Shenghua, et al. Multi-kernel support vector machine considering sample optimization selection for analysis and evaluation of landslide disaster susceptibility[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2034-2045.

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