首页|基于多变量时空融合网络的风机数据缺失值插补研究

基于多变量时空融合网络的风机数据缺失值插补研究

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
风电场数据的完整性会因恶劣天气、输入信号丢失、传感器故障等原因遭到破坏，而大面积的数据缺失将给风机设备的运行和维护带来严峻考验。因此，提出一个多变量时空融合网络(Multivariate spatiotemporal integration network，MSIN)来解决缺失数据问题。首先，提出包含缺失值定位-指引机制的MSIN结构，揭示缺失部分数据的潜在信息，确保插补数据符合真实分布。其次，在网络中设计多视角时空卷积模块，捕捉同一风机多个变量与多个风机同一变量之间的局部空间和全局时间相关性，用于提高插补数据的真实性。接着，提出网络实时自更新机制，根据风电场实时变化情况实现在线调整，能够提升网络泛化能力，由此弥补重新训练模型的时间和空间成本高的缺陷。最后，通过真实的风机数据验证所提网络的有效性和优越性。相关分析结果表明，相较于MissForest等传统数据插补方法的插补性能，平均绝对误差(Mean abso-lute error，MAE)、平均绝对百分比误差(Mean absolute percentage error，MAPE)和均方根误差(Root mean square er-ror，RMSE)分别下降 18。54％、41。00％和 3。15％以上。
Study of Missing Value Imputation in Wind Turbine Data Based on Multivariate Spatiotemporal Integration Network
The integrity of wind farm data can be damaged by bad weather,input signal loss,sensor failure,etc.,and the large-scale data loss will bring severe tests to the operation and maintenance of wind turbine equipment.Therefore,this paper proposes a multivariate spatiotemporal integration network(MSIN)to solve the missing data problem.Firstly,the structure of MSIN is proposed to include a localization guidance mechanism for missing values,which reveals the potential information of the missing part of the data and ensures that the imputed data conforms to the true distribution.Secondly,a multi-view spatiotemporal convolution module is designed in the network to capture the local spatial and global temporal correlations between multiple variables of the same wind turbine and the same variable of multiple wind turbines,which is used to improve the realism of the imputed data.Then,a real-time self-updating mechanism is proposed to adjust the network online according to the real-time changes of wind farms,which can improve the generalization ability of the network and thus make up for the defect of high time and space costs when retraining the model.Finally,the effectiveness and superiority of the proposed network are veri-fied by real wind turbine data.The results show that the mean absolute error(MAE),the mean absolute percent-age error(MAPE),and the root mean square error(RMSE)are reduced by more than 18.54％,41.00％and 3.15％,respectively,when compared with the traditional data imputation methods such as MissForest and so on.

Wind turbine datadata imputationspatiotemporal characteristicsgenerative adversarial networks

詹兆康、胡旭光、赵浩然、张思琪、张峻凯、马大中

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东北大学信息科学与工程学院 沈阳 110819

山东大学电气工程学院 济南 250100

风机数据 数据插补 时空特征 生成对抗网络

国家自然科学基金国家自然科学基金国家自然科学基金中央高校基本科研业务费中央高校基本科研业务费辽宁省自然科学基金

U22A202216230310362073064N2304017N22040072022-KF-11-02

2024

10.16383/j.aas.c230534

自动化学报
中国自动化学会 中国科学院自动化研究所

自动化学报

CSTPCD北大核心
影响因子：1.762
ISSN：0254-4156
年,卷(期)：2024.50(6)
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