Research On Condition Monitoring And Fault Identification Of Double-fed Wind Turbine Transmission

In order to mitigate the global warming trend and prevent energy crisis,wind energy is a kind of renewable clean energy that has attracted the attention of countries all over the world.With the development of wind power technology,some problems in the operation process of wind turbines have become increasingly prominent.In the faults of wind turbines,the longest downtime caused by transmission chain faults and the large economic losses are caused.Therefore,it is of great necessity to conduct state monitoring and status assessment of wind turbine transmission chains.This paper discusses the research significance of the status monitoring and fault identification of wind turbine generator trains and the research status at home and abroad.It analyzes the typical fault types,fault causes and fault characteristics of the transmission chain,and studies the characteristic parameters of time and frequency domain analysis.method.The wavelet analysis and Hilbert envelope analysis theory are introduced.The vibration signals of the rolling bearing in the normal,inner ring spalling and outer ring spalling conditions are analyzed by wavelet analysis and Hilbert envelope decomposition,and the rolling bearing is drawn under normal conditions and inside and outside.Spectral maps in the case of flaking,analysis and comparison of the vibration signal waveforms in different operating conditions,find out the characteristics of the vibration signal waveform when the inner ring peels off and the outer ring peels off.Because the health status of the drive chain is evaluated through a single index,the effect is not very satisfactory.In order to more accurately diagnose various types of faults,the vibration signal of the rolling bearing is in two aspects: time domain and frequency domain.The four parameters are indicators,and a BP neural network system is established on the matlab platform to realize the fault identification of rolling bearing faults.