A novel fault‐detection methodology of proposed reduced switch MLI fed induction motor drive using discrete wavelet transforms

Abstract

Summary Induction motors are typically promoted in industrial applications by adopting energy‐efficient power‐electronic drive technology. Multilevel inverters (MLI) have been widely recognized in recent days for high‐power, medium‐voltage‐efficient drives. There has been vital interest in forming novel multilevel inverters with reduced switching elements. The newly proposed reduced‐switch five‐level inverter topology extends with fewer switches, low dv/dt stress, high efficiency, and so on, over the formal multilevel inverter topologies. The multilevel inverter's reputation is greatly affected due to several faults on switching elements and complex switching sequences. In this paper, a novel fault identification process is evaluated in both healthy and faulty conditions using discrete‐wavelet transform analysis. The discrete wavelet transform utilizes the multi‐resolution analysis with a feature extraction methodology acquired for fault identification over the classical methods. A novel fault identification scheme is implemented on reduced‐switch five‐level MLI topology using the Matlab/Simulink platform to increase the drive system's reliability. The effectiveness of simulation outcomes is illustrated with proper comparisons. The proposed topology's hardware model is implemented using a dSPACE DS1103 real‐time digital controller and the results of the experiment are presented.

Energy management control strategies for energy storage systems of hybrid electric vehicle: A review

“…Hence the cage rotor IM is commonly stated as the IM. In general, the IMs are operated in two modes namely field‐oriented control (FOC) and the control of the variable‐voltage variable‐frequency (VVVF) 24 …”

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

“…CM of brushless DC motors is investigated based on energies for characteristic frequencies based on both STFT and WT in [17]. A decomposition rate is used in [18] for CM of electric drives based on WT. RMS and Kurtosis are calculated for the WT coefficients for broken bar fault detection in electric drives and combined with a neural network for fault classification in [19].…”

A Layering Linear Discriminant Analysis-Based Fault Diagnosis Method for Grid-Connected Inverter

“…The signal-based fault diagnosis method depends on the signal distortion caused by the fault [14]. The distinguishing fault features are extracted by signal processing methods such as Fourier transform [15][16][17], wavelet decomposition [18,19], and variational modal decomposition [20,21]. The extracted features are often used as input for an intelligent algorithm, and the intelligent algorithm finally identifies the fault types.…”