This paper presents a methodology for the broken rotor bars fault detection is considered when the rotor speed varies continuously and the induction machine is controlled by Field-Oriented Control (FOC). The rotor fault detection is obtained by analyzing a several mechanical and electrical quantities (i.e., rotor speed, stator phase current and output signal of the speed regulator) by the Discrete Wavelet Transform (DWT) in variable speed drives. The severity of the fault is obtained by stored energy calculation for active power signal. Hence, it can be a useful solution as fault indicator. The FOC is implemented in order to preserve a good performance speed control; to compensate the broken rotor bars effect in the mechanical speed and to ensure the operation continuity and to investigate the fault effect in the variable speed. The effectiveness of the technique is evaluated in simulation and in a real-time implementation by using Matlab/Simulink with the real-time interface (RTI) based on dSpace 1104 board.

Discrete wavelet transform and energy eigen value for rotor bars fault detection in variable speed field-oriented control of induction motor drive.

This study proposes a new approach for the detection of broken rotor bar (BRB) fault in three phase induction motor drive using Hilbert transform (HT) and artificial neural networks (ANNs), where the machine is controlled by direct torque control (DTC). HT is preferred to develop the stator current envelope. The sideband frequency and its amplitude of the samples are the input for the ANN. By using fast Fourier transform, the amplitude and frequency components are extracted and the severity of fault is determined by comparing the magnitude of an average of sideband frequency with the fundamental frequency. High accuracy identification of fault is found by ANN, where the results are trained and tested to a minimum mean square error that will detect the number of BRB in the induction motor. DTC is adopted for a suitable control technique in the industrial drives system to maintain good performance in torque control. The performance of the proposed method is verified by using MATLAB/SIMULINK and experimental tests.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-pel.2019.1543

Broken rotor bar fault detection using Hilbert transform and neural networks applied to direct torque control of induction motor drive

In this study, a turn-to-turn short circuit of motor stator fault diagnosis system based on deep auto-encoder and soft-max classifier is proposed. It is also considered in the proposed system that an unbalanced power supply has similar current characteristics and is possible to be misidentified. The determination of neural network parameters and their effect in training are given systematically, which improves the performance compared with traditional auto-encoder. The proposed fault diagnosis system can map the preprocessed three-phase motor current signals to a two-dimensional vector, corresponding to certain area of the feature plane to identify different fault types. The proposed system is verified via simulation experiment using MATLAB and physical experiment using a motor in laboratory. The conclusion illustrates that when signals originating from discrete motor speed, load, and fault severity are used in training, the fault diagnosis is still effective in continuous state. The accuracy is 89% for speed verification and 95% for load verification in simulation and is above 99% in the physical experiment.

Turn-to-turn short circuit of motor stator fault diagnosis in continuous state based on deep auto-encoder

http://profdoc.um.ac.ir/articles/a/1028175.pdf

Design and experimental verification of a dead beat power control strategy for low cost three phase PWM converters

Advanced power conditioner using sinewave modulated buck–boost converter cascaded polarity changing inverter

This paper highlights the voltage sag and swell of the power quality issue and Unified power flow controller (UPFC) is used for mitigation. The switching level model is constructed using three phase six pulse shunt and series converters. Both the converters are modeled as back to back voltage source inverters connected with the D.C link are controlled by sinusoidal pulse width modulation scheme. This model is implemented in single machine infinite parallel transmission line system. The detailed UPFC simulation in switching level model is performed in Matlab/Simulink environment. The result shows that UPFC controller is capable of mitigating the voltage sag and swell in the proposed system.

Power quality analysis and mitigation case study using Unified Power Flow Controller

This paper attempts to present a concise review of the research done around fault detection methods for IMs. Various methods to diagnose faults researched over past few years are reviewed and presented with the focus on state-of-the-art fault diagnosis that use on-line (while the machine is running) and non-intrusive (without requiring sensors) techniques. The speed and accuracy of the fault diagnosis requires signal analysis using DSPs. The signal analysis algorithms such as Fourier Transform and Wavelet Transform are studied and compared. Also for the sake of completeness the other traditional fault diagnosis techniques have been discussed. Current research is exploring Partial discharge, Winding Function Theory, and Expert Systems for fault diagnosis and these techniques are also mentioned. Reviewing of the main options for induction machines fault detections and comparing has been the main consideration of this paper. Mainly, in case of traction systems the new techniques are essential to avoid inadvertent shutdown.

Diverse fault detection techniques of three-phase induction motor — A review

A novel component minimized converters for unified power flow controller

Inverter fed Induction motor drives are deployed across a variety of industrial and commercial applications. Although the drives in the question are well known for their reliable operation in any type of environment, it becomes an important daunting critical task to have them in continuous operation as per the applications’ requirement. Identifying the faulty behavior of power electronic circuits which could lead to catastrophic failures is an attractive proposition. The cost associated with building systems devoted for monitoring and diagnosis is high, however such cost could be justified for the safety-critical systems. Commonly practiced methods for improving the reliability of the power electronic systems are: designing the power circuit conservatively or having parallel redundant operation of components or circuits and clearly these two methods are expensive. An alternative to redundancy is fault tolerant control, which involves drive control algorithm, that in the event of fault occurrence, allows the drive to run in a degraded mode. Such algorithms involve on-line processing of the signals and this requires Digital Signal Processing of the signals. This paper presents the FFT and Wavelet transform techniques for on-line monitoring and analyzing the signals such as stator currents.

Incipient Fault Detection of the Inverter Fed Induction Motor Drive

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Papers

Power quality analysis and mitigation case study using Unified Power Flow Controller

Diverse fault detection techniques of three-phase induction motor — A review

A novel component minimized converters for unified power flow controller

Incipient Fault Detection of the Inverter Fed Induction Motor Drive