摘要
针对风电机组关键部件的运行状态识别问题,本文提出一种引入时间维度信息的降噪自编码器融合多个传感器信号进行关键部件运行状态识别的方法.首先通过训练学习SCADA(supervisory control and data acquisition)数据中各传感器信号之间相互关系以及时间维度上运行状态变化趋势,建立关键部件正常运行状态模型;然后将风电场实时采集数据输入模型,再根据模型输出残差的分布情况识别关键部件的运行状态.最后使用风电场中发电机和齿轮箱两种关键部件实际故障数据进行验证,结果表明融合时间维度上的状态变化趋势更准确的描述了关键部件运行状态,有效提高了关键部件运行状态识别的准确度.
To identify the operational state of key components in the wind turbine,we propose a denoising autoencoder with time-dimension information for fusing the multiple sensor signals of these key components.Firstly,the relationship between the signals of each sensor in the SCADA data is learned and the change trend of the operational state in the time dimension is obtained to establish a normal model of the key components.Then,real-time data collected by the wind farm is input into the normal model,and the operational state of the key components are identified based on the distribution of the output residual of the normal model.Finally,we use actual fault data from the generator and gearbox in the wind farm for verification.The results show that obtaining the state change trend in the time dimension improves the ability to accurately describe the operational state of the key components,and the proposed method effectively improves the accuracy of the identification of the operational state of these key components.
作者
苏连成
郭高鑫
SU Liancheng;GUO Gaoxin(School of Electrical Engineering,Yanshan University,Qinhuangdao 066000,China)
出处
《信息与控制》
CSCD
北大核心
2021年第3期337-342,349,共7页
Information and Control
基金
国防基础研究计划资助项目(JCKY2019407C002)。
关键词
风电机组
运行状态识别
信息融合
时窗降噪自编码器
wind turbine
operation state identification
information fusion
time window denosing auto-encoder(TW-DAE)
