摘要
针对海上风电场和高海拔地区风机机组的叶片覆冰故障模型精度低、建模速度慢等问题,提出一种基于LeNet5like的迁移学习风电机组叶片覆冰故障诊断方法。首先,整合监控和数据采集系统的记录数据与风机覆冰情况进行预处理,建立训练数据集;其次,基于改进后的LeNet5like网络构建覆冰故障诊断模型,提取数据集中多变量间的相关性特征信息;然后,经网络参数微调迁移学习对模型进行训练,实现对其他风机覆冰故障诊断模型的快速建立;最后,经实验验证,该模型覆冰故障诊断准确率为98.90%,较无迁移模块网络训练时间缩短28 s,提升约15.91%,验证了基于LeNet5like的迁移学习风电机组叶片覆冰故障诊断方法的精确性和快速性。
A fault diagnosis of wind turbine icing characteristics based on LeNet5like transfer learning method is proposed,to address the problems of low accuracy and slow modelling speed of icing characteristics fault models,which wind turbine units are in offshore wind farms and high altitude areas.Firstly,the recorded data from the SCADA system and the wind turbine icing situation are pre-processed to build a training dataset;secondly,the icing fault diagnosis model is constructed based on the improved LeNet5like network to extract the correlation feature information between multiple variables in the dataset;then,the model is trained by the transfer learning fine-tuning to achieve the rapid establishment of ice-cover fault diagnosis models for other wind turbines;finally,the model is experimentally validated to have an icing fault diagnosis accuracy of 98.90%,a 28 s reduction in training time and an improvement of about 15.91%over the transfer module-free network,verifying the accuracy and speed of the LeNet5like based transfer learning wind turbine blade ice-cover fault diagnosis method.
作者
吕游
封烁
郑茜
邓丹
刘吉臻
Lyu You;Feng Shuo;Zheng Xi;Deng Dan;Liu Jizhen(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University,Beijing 102206,China;School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2024年第3期128-143,共16页
Chinese Journal of Scientific Instrument
基金
中央高校基本科研业务费专项资金(2023MS029)资助。
