WAVELET BASED STATOR INTER-TURN FAULTS DETECTION IN THREE-PHASE INDUCTION MOTORS OPERATED UNDER NOISY CONDITION

Abstract

The motor current signature analysis is widely used for diagnosis of various industrial motor faults. Generally, the captured current signals are corrupted by noise. In order to extract the fault feature from noisy current signals, the corrupted current signal has to be preprocessed by some means. This paper proposes a wavelet and adaptive threshold based approach to detect and identify stator inter-turn faults in 3-phase induction motor. A stationary wavelet transform is used to find the fault residue present in the three phase stator currents. These three phase residue currents are again decomposed with discrete wavelet transform to extract the disturbance. Fault index and three phase energies are defined and compared with adaptive thresholds to detect the transients and its location. The sensitivity of stator inter-turn fault to the number of shortened turns is also analysed. Finally, the algorithm is tested with both simulation as well as practical data for various levels of stator inter-turn faults. The validity and effectiveness of the proposed algorithm is clearly shown from obtained results.