文档介绍:中国测试
China Measurement & Test
ISSN 1674-5124,CN 51-1714/TB
处理,能够保障机组的安全稳定运行,提升风电场经济效益。通过 SCADA 运行数据进行风电机组故障诊断是一种重要
的故障诊断诊断方式,文章从故障诊断的特征提取及故障诊断模型构建角度出发,提出应用随机森林袋外估计(OOB)
进行特征选择的特征提取方法和改进参数优化机器学****算法(BO-LightGBM)的风电机组故障诊断模型,提高了基于数据
驱动的风电机组故障预测的精度。通过风电场实际运行数据对所提故障诊断方法进行验证,结果证明该模型对于不同
类型的故障均有 92%以上的预测准确性,表明该模型对风电机组故障诊断具有较好的适用性。
关键词:风电机组;故障诊断;机器学****特征提取;贝叶斯优化
中图分类号:TK83 文献标志码:A
Fault diagnosis method of wind turbine based on OOB-BO-LightGBM
ZHANG Hang1,SHI Zhaopei1,SHU Yin1,ZHANG Zirui1,SONG Zhiqiang1,XU Chang2
(1.China Three Gorges Renewables (Group) Co., Ltd, Beijing 101100,China; 2.College of Energy and
Electrical Engineering, Hohai University, Nanjing 211100,China)
Abstract: With the large-scale grid-connected operation of wind turbines, fault diagnosis of wind turbines has gradually
become a research hotspot in the industry. The timely detection and treatment of wind turbine faults can ensure the safe and
stable operation of the wind turbines and imp