基于多变量时空融合网络的风机数据缺失值插补研究OA北大核心CSTPCD

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.