Study On Condition Monitoring Method Of Wind Turbine Gearbox Based On Deep Learning And SCADA Data Fusion

Wind turbines are usually located in remote areas with rich wind resources,such as mountains,deserts and sea.The bad environment and complex and changeable working conditions make wind turbine failures occur frequently.Among all kinds of wind turbine failures,the gearbox failure of transmission system causes the greatest loss.Therefore,it is of great significance to monitor the operation status of wind turbine gearbox.At present,the wind farm is basically equipped with supervisory control and data acquisition(SCADA)and condition monitoring system(CMS)to monitor wind turbines,however,CMS contains coupling vibration signals of multiple components.The vibration signals are greatly affected by the working conditions,the information is single and the sampling interval is long,so it is difficult to establish the threshold for effective condition monitoring.In order to carry out real-time condition monitoring through more variables and threshold,this paper uses the SCADA system with small sampling interval and more variables for gearbox condition monitoring.Traditional monitoring methods are mainly realized by using signal processing and machine learning methods to analyze and monitor the operation data provided by SCADA system.However,with the combination of SCADA system and big data technology,new problems are brought to gearbox condition monitoring.On the one hand,the data quality is uneven,and there are a lot of poor quality data and missing data in the original data,so it is difficult to directly mine valuable data On the other hand,there is a large number of gearbox SCADA data,and there is a complex coupling relationship between different variables,so it is difficult for traditional monitoring methods to fully learn a large number of data.Aiming at the problems of data quality and condition monitoring method of wind turbine gearbox,this paper uses the wind turbine mechanism combined with deep learning method to preprocess the wind turbine SCADA data,and then constructs a memory enhanced self coding deep learning model to monitor the condition of wind turbine gearbox combined with SCADA data.In this paper,the following studies are carried out.Firstly,aiming at the problem of poor quality of SCADA raw data,the data cleaning method of wind turbine operation mechanism combined with bin algorithm,the data repair method of gate recurrent unit(GRU)based on adjacent ratio unit and the design of data preprocessing strategy are proposed.The bad SCADA data of gearbox is removed by f wind turbine operation mechanism combined with bin algorithm,and then the missing data is repaired by GRU repair model.The effect of pretreatment method is verified by actual SCADA data set.The results show that the pretreatment method can greatly improve the quality of SCADA data.Secondly,aiming at the problems of poor learning ability and poor monitoring effect of traditional gearbox condition monitoring methods,a wind power gearbox condition monitoring model based on memory enhanced deep autoencoder(MAE)and SCADA data is proposed.According to the monitoring process of wind turbine gearbox,Pearson correlation coefficient is calculated through the cleaned data samples,and SCADA variables required by the model are selected.Then memory enhanced self coding monitoring model is constructed to learn data features.SCADA data reconstruction error is used as the indicator of condition monitoring,and combined with exponentially weighted moving average Moving average(EWMA)control chart.The experimental results show that this model has better monitoring effect than the traditional monitoring model.Finally,the wind turbine gearbox monitoring function module is developed and deployed on the wind turbine condition monitoring micro service platform of a wind farm.The model proposed in this paper is used to build related services,and the function,interface,database and data model of the monitoring service are designed.The effectiveness of the wind turbine gearbox monitoring function module is verified through the visual front-end of the micro service platform.