Research On Diagnosis Method Of Composite Fault Of Air-gap Static Eccentricity And Interturn Short Circuit For Generators

With the development of society,every aspect of our life is full of electricity.As the core equipment of power production,the safe operation of generators is of great significance to the orderly operation of the electrical system,and even to the stability and harmony of the society.For the static eccentricity fault and inter-turn short circuit fault of the generator,the stator vibration signal of CS generator is taken as the research object,and it is also analyzed by the proposed method in this paper to realize the enhanced detection of the weak fault characteristics of inter-turn short circuit,and the diagnosis and identification of different types of static eccentricity fault、rotor short circuit and radial eccentricity composite fault.Aiming at the three kinds of air gap static eccentricity faults of radial eccentricity,axial eccentricity and mixed eccentricity,the ALIF-KFCM method is used to diagnose the fault signals,whose clustering map and evaluation index will be compared with the results of methods of EEMD-KFCM,CEEMDAN-KFCM and ALIF-FCM.What’s more,this process can qualitatively and quantitatively verify that the proposed method can well diagnose and identify the faults of radial eccentricity,axial eccentricity and mixed eccentricity,and has certain effectiveness and superiority in the aspect of static eccentricity fault diagnosis.Under the short circuit fault,the generator’s stator will produce one to four times frequency radial vibration.However,due to the low signal to noise ratio and the problem that the effective information is prone to be submerged,the Minimum entropy deconvolution envelope spectrum method with enhanced multipoint optimal adjustment is used to enhance the feature of rotor short circuit signals,whose envelope spectrum and amplitude enhancement ratio of feature frequency also will be compared with the results of the enhanced maximum correlation kurtosis deconvolution envelope spectrum and the enhanced frequency weighted energy operator envelope spectrum.This process qualitatively and quantitatively verifies that the proposed method in this paper has certain advantages in enhancing the features of rotor short circuit signals.Under the short circuit fault of generator stator,the stator will produce radial vibration of double frequency,four frequency and six frequency.In View of this characteristic,the method based on EEMD and 1.5-dimensional Teager energy operator is used to enhance stator short circuit fault characteristics.Besides,its frequency spectrum and amplitude enhancement ratio of characteristic frequency are compared with the results based on EEMD and FWEO,EEMD and Teager energy operator,which qualitatively and quantitatively analysis shows that the proposed method is more effective in noise filtering and enhancement of stator short circuit fault characteristics.Aiming at the combined fault of rotor short circuit and radial eccentricity of generator,the VMD-KFCM method is used to process the normal signal,rotor short circuit,radial static eccentricity and their combined fault respectively.The clustering diagram and clustering index are compared with the results based on EEMD-KFCM,ALIF-KFCM and VMD-FCM.The qualitative and quantitative results show that the proposed method can better diagnose and identify the composite fault of rotor short circuit and radial static eccentricity,and has certain effectiveness and superiority.