可再生能源2026，Vol.44Issue(1)：70-77,8.
多传感器数据融合的风力机侧风状态评估
Cross-wind state evaluation of wind turbine based on multi-sensor data fusion
摘要
Abstract
In order to explore the strain characteristics of wind turbine blades during continuous crosswind,a multi-sensor data fusion method for wind turbine crosswind state evaluation is proposed in this study.In this method,ensemble Empirical Mode Decomposition(EMD)and composite multi-scale permutation entropy-wavelet algorithm are used to jointly reduce the noise of wind turbine blade strain signals,and then Kernel Principal Component Analysis(KPCA)is used to integrate the multiple groups of strain signals after noise reduction.The Squared prediction error(SPE)statistic was used as the evaluation index to effectively divide the wind turbine crosswind process.The results show that the proposed method is effective in reducing the noise of non-stationary wind turbine blade strain signal,and can accurately reflect the strain variation law in continuous crosswind process.In addition,through the combination of KPCA and SPE statistics,the process of wind turbine crosswind is segmtioned,and the operating state of wind turbine crosswind under different influencing factors is analyzed.关键词
叶片应变/连续侧风过程/降噪处理/多传感器数据融合Key words
blade strain/continuous crosswind process/noise reduction processing/multi-sensor data fusion分类
能源科技引用本文复制引用
李勇博,刘珍,汪建文,郑梦楠,刘鸿宇..多传感器数据融合的风力机侧风状态评估[J].可再生能源,2026,44(1):70-77,8.基金项目
省部级基本科研业务费项目(JY20220247). （JY20220247）
