摘要
“大数据”时代的到来为风机健康监测带来了新机遇,风机往往运行在极端恶劣环境下,监测数据中夹杂了大量缺失值,数据质量无法保障,进而会制定有误的运维指导策略。为保证风速监测数据质量,提出了基于高维时空张量CP分解的风速监测数据缺失值恢复方法。构建包含时空信息的四阶张量,利用CP分解将张量分解为多个因子矩阵,通过加权张量将恢复缺失数据转化为求解目标函数最小值,根据因子矩阵重构张量,从而获得缺失处原始信息值。利用提出方法与GPR、GRU、LSTM、SWLSTM等传统方法对某风电场的缺失数据进行恢复,结果表明,相比传统方法,提出方法的R^(2)最接近1,MAE等误差指标均为最小,具有最高拟合度,从而验证了该方法的有效性。
The advent of“big data”era brings new opportunities for wind turbine health condition monitoring.However,wind turbines often operate in harsh environment,and thus monitoring data is mixed with a large number of missing values which reduce data quality.As a result,wrong operation and maintenance strategies will be developed based on these low-quality data.A method based on CP decomposition of spatial-temporal tensor was proposed to recover missing data to improve the quality of monitoring data.Firstly,a four-dimension tensor containing spatial-temporal information was constructed.Then CP decomposition was applied to decompose the estimating tensor into factor matrices.Afterwards,a weighted tensor was used to translate the recovery issue into the solving of a minimization function.Finally,the tensor was reconstructed according to the factor matrices,and the original value of the missing data was obtained.The actual monitoring data of a wind farm was used to recover the missing values by different methods including GPR,GRU,LSTM,SWLSTM.The results show that compared with that of traditional methods,the R^(2) of proposed method is closest to 1 and the other recovery error such as MAE are minimum,which has higher fitting degree with the real data.Therefore,the case verifies the effectiveness of the proposed method.
作者
许学方
胡诗婷
时培明
李瑞雄
李志
XU Xuefang;HU Shiting;SHI Peiming;LI Ruixiong;LI Zhi(School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,China;School of Energy and Power Engineering,Xi’an Jiaotong University,Xi’an 710049,China;Jiangsu Goldwind Science&Technology Co.,Ltd.,Yancheng 224100,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2023年第11期163-169,共7页
Journal of Vibration and Shock
基金
秦皇岛市科学技术研究与发展计划(202101A345)
河北省自然科学基金青年项目(E2022203093)
国家自然科学基金项目(61973262)
燕山大学基础创新科研培育项目(2021LGQN022)。
关键词
风机健康监测
数据质量
缺失值数据
张量分解
数据恢复
health condition monitoring of wind turbine
data quality
data with missing values
tensor decomposition
data recovery
