自动化学报2024，Vol.50Issue(6)：1171-1184,14.DOI:10.16383/j.aas.c230534
基于多变量时空融合网络的风机数据缺失值插补研究
Study of Missing Value Imputation in Wind Turbine Data Based on Multivariate Spatiotemporal Integration Network
摘要
Abstract
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.关键词
风机数据/数据插补/时空特征/生成对抗网络Key words
Wind turbine data/data imputation/spatiotemporal characteristics/generative adversarial networks引用本文复制引用
詹兆康,胡旭光,赵浩然,张思琪,张峻凯,马大中..基于多变量时空融合网络的风机数据缺失值插补研究[J].自动化学报,2024,50(6):1171-1184,14.基金项目
国家自然科学基金(U22A20221,62303103,62073064),中央高校基本科研业务费(N2304017,N2204007),辽宁省自然科学基金(2022-KF-11-02)资助 Supported by National Natural Science Foundation of China(U22A20221,62303103,62073064),Fundamental Research Funds for the Central Universities in China(N2304017,N2204007),and Natural Science Foundation of Liaoning Province(2022-KF-11-02) （U22A20221,62303103,62073064）
