Research On Wind Turbine Fault Early Warning Technology Based On Deep Learning

With the rise of new energy power generation technology,the installed capacity of wind turbines continues to increase.At the same time,the potential faults of wind turbines have also increased with the increase of wind turbines.Therefore,early prediction of potential faults of wind turbines and ensuring the safe and stable operation of wind turbines is of great significance for improving power generation efficiency and reducing maintenance costs.For this reason,this paper builds a fault early warning model of wind turbines based on deep learning theory,and early warning of potential faults of wind turbines can effectively predict possible faults.In order to realize the fault early warning of the wind turbine gearbox,a fault early warning method of the wind turbine gearbox based on Convolutional Neural Networks(CNN)is proposed.By extracting vibration data that can reflect the operating status of the wind turbine gearbox,and combining the characteristics of the sample features that can be enhanced and extracted with the convolutional neural network,a fault early warning model of the wind turbine gearbox based on the convolutional neural network is constructed,and two levels of fault early warning indicators are set.The performance of the proposed fault early warning method was tested using engineering case analysis to effectively predict the potential failure of the gearbox of the wind turbine.In order to realize the fault early warning of the main bearing of the wind turbine,a method of early warning of the main bearing of the wind turbine based on Stacked Autoencoder(SAE)is proposed.The stacked autoencoder extracts the deep features of the sample data layer by layer,and transforms the scattered data into the intrinsic features that can deeply describe the sample data.First,the time domain characteristics of the vibration data of the main bearing of the wind turbine and the data directly reflecting the operating state of the main bearing are extracted as data samples;Secondly,construct a fault early warning model for the main bearing of the wind turbine,and give detailed steps for fault early warning;Finally,this method is tested through an engineering example,and the effect of the traditional neural network model on the early warning of the wind turbine main bearing is analyzed.The results show that the method proposed in this paper has better performance,higher accuracy and early warning of failures.