A Novel Technique Using Multiresolution Wavelet Packet Decomposition for Real Time Diagnosis of Hunting in Line Start IPM Motor Drives

Abstract

A line-start interior permanent magnet (LSIPM) motor operates with inherent hunting phenomena. This paper presents a novel procedure for real-time diagnosis of hunting in LSIPM motors based on stator current signatures. Torsional vibration associated with hunting in a LSIPM motor influences the electrical supply, and introduces variable amplitude lower and upper sidebands in the stator current. In this paper, wavelet packet decomposition is applied to perform multiresolution analysis of the nonstationary current signals for detection and extraction of the sideband frequency components superimposed on the fundamental. Statistical techniques are applied on the extracted data signals and a signature for the diagnosis of hunting phenomenon is established. The proposed technique is validated by performing finite element analysis (FEA) of two three-phase four-pole LSIPM motors of different power ratings for various case scenarios. Experimental investigations have been carried out for a three-phase four-pole 208-V, 1-HP LSIPM motor drive in order to validate the performance of the proposed method under various operating conditions. Based on FEA and experimental results, the proposed technique can successfully diagnose the hunting phenomenon without a vibration sensor.