The invention discloses an air gap eccentricity fault diagnosis and classification method of an ANFIS
wind power double-fed asynchronous motor, belonging to the field of motor state detection and fault diagnosis.
Wind power double-fed asynchronous motor air gap eccentricity faults are divided into several frequent fault types, based on
software simulation. A double-fed asynchronous
motor model is simulated and several fault types when an air gap eccentricity happens are simulated. The changes of current in a
stator winding under different eccentricities of moving and static eccentric faults, a
time domain is converted into a
spectrogram when the
wavelet decomposition of the current is carried out, characteristic frequency bands when different faults happen are extracted, the fault characteristic frequencies corresponding to different types of the air gap eccentricity faults are analyzed, then the
wavelet energy of the bands are used as training sample data, an adaptive
neural fuzzy inference system for the double-fed asynchronous motor air gap eccentricity faults is constructed, a
hybrid learning
algorithm is introduced to carry out training, and the air gap eccentricity fault type of the double-fed asynchronous motor is judged. The method has the advantages of high precision and high
operability.