Stator Current Of Induction Motor Research Articles

A Hybrid Technique for the Detection of Broken Rotor Bar of Induction Motor

A hybrid technique for diagnosing broken rotor bar fault of induction motor using Multi-Wavelet Transform (MWT) and radial basis neural network is presented. The stator currents of induction motor are preprocessed using multi-wavelet transform and the decomposed components are obtained. Then, these features are given as input to the neural network to identify fault. This paper compares the proposed hybrid technique with MWT-Feed Forward Neural Network (FFNN) and Discrete Wavelet Transform-FFNN techniques. These techniques are compared using the concept of classifier performance. From the simulation results, it is evident that the proposed method is superior to other methods with regard to objective proposed.

The Structural Robustness of the Induction Motor Stator Currents Subsystem

AbstractStator‐currents control is essential for several high‐performance induction motor control schemes such as field oriented control. There are numerous reports dealing with sophisticated control schemes for this subsystem. However, classical linear controllers remain widely used due to their experimental success and simplicity. Considering that the induction motor stator currents subsystem is normally represented by a fifth order non‐linear multivariable model, it is remarkable that simple fixed linear controllers, such as typical proportional integral schemes, are able to provide adequate robustness and performance in practice. In fact, it is normally assumed that this subsystem is “easy” to control, and the difficulties are mostly technical. Moreover, it is common practice to consider a stable first order linear single input single output (SISO) system as a design model. On the other hand, it is widely known that stable and minimum phase uncertain SISO systems are also “easy” to control. In this article it is formally demonstrated that the stator currents subsystem of the induction motor is the multivariable equivalent of such SISO systems. That is, it is formally demonstrated that this process is “easy” to control. This result may assist with better induction motor control and may serve as an example of the evaluation of similar multivariable systems. Real time experimental results are included.

Analysis of stator current of induction motor used in transport system at single phasing by measuring phase angle, symmetrical components, Skewness, Kurtosis and harmonic distortion in Park plane

Stator current of induction motor is analysed at single phasing by measuring phase angle, symmetrical components, Skewness, Kurtosis and harmonic distortion in Park plane. Diagnosis of single phasing of induction motor has been achieved by sequence components and stator current based pattern assessment. Distinct phase shift from 120° to 180° is observed between healthy phases. Patterns are formed using line currents and single phasing is assessed by using rule sets developed from features extracted from those patterns. Experimental result agrees with rule set and result obtained by assessment using sequence components. Then assessment is done by analysing wavelet decomposition based Skewness and Kurtosis of stator current in Park plane. At last, area based approach is used for assessment of single phasing and different distortion factors because of harmonics in Park plane are assessed. Accuracy increases with the increase of sampling rate of data acquisition system and use of Park plane has reduced computation effort by two-thirds. Thus single phasing is detected by sequence components supported by pattern formation and feature extraction and then harmonics during single phasing are assessed by wavelet decomposition and area-based approach in Park plane.

Fault diagnosis in six-phase induction motor using a current trajectory mass center

This paper proposes a new methodology for diagnosing the occurrence of stator winding faults in the six-phase induction motor. The proposed approach uses the xy current trajectory mass center of the motor stator currents. The Park transform is applied to the acquired induction motor stator currents. This transformation allows obtaining specific patterns that are used to identify stator winding faults. For healthy motors, a single point in the xy-plane is obtained. However, for a motor with a stator winding fault a circle is obtained, whose radius is related with the severity of the fault. To identify these patterns an algorithm, entitled current trajectory mass center, was developed. A theoretical analysis of the six-phase motor in αβ and xy current coordinates, for healthy and stator fault operation modes, is also presented. In order to show the applicability of the proposed technique several simulation and experimental results are presented.

Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive

n speed sensorless controlled induction motor drive, the rotor flux based Model Reference Adaptive System (MRAS) for is more popular. In MRAS, the voltage model equations are used in the reference model, which leads to poor performance of the drive at low speeds. In this paper, a new stator current based MRAS is presented. In this measured stator currents of induction motor are used as a reference model to avoid the use of a pure integrator. Estimated stator currents are used as an adjustable model. The estimation of stator currents requires the information of the rotor flux which can be obtained from the voltage or current model. This encounters instability and dc drift. To overcome these problems, flux observer is proposed based on Neural Network (NN). This offline trained multilayer feed-forward NN flux observer applied to indirect vector controlled speed sensorless induction motor drive has been verified by MATLAB/SIMULINK.

Five-phase AC/DC/AC converter with pulse-width modulation rectifier

The paper presents an AC/DC/AC power electronic converter system with a five-phase two-level active rectifier and a five-phase two-level inverter with a DC-link. This is an extension of a three-phase active rectifier with two additional legs. This topology may find its application in five-phase variable speed generator such as wind electric generator system and in multi-phase electric drive systems. The front end converter has two control loops; the DC-link voltage control and the source side current control with controllable power factor operation. This paper discusses the multiphase active rectifier applications in multi-phase motor drive system. Simple hysteresis controllers are used to control the input from the source and output to the load currents (five-phase induction motor stator current). The validity of the control is validated using analytical and simulation approaches.

Five-Phase AC/DC/AC Converter with Pulse-Width Modulation Rectifier

The paper presents an AC/DC/AC power electronic converter system with a five-phase two-level active rectifier and a five-phase two-level inverter with a DC-link. This is an extension of a three-phase active rectifier with two additional legs. This topology may find its application in five-phase variable speed generator such as wind electric generator system and in multi-phase electric drive systems. The front end converter has two control loops; the DC-link voltage control and the source side current control with controllable power factor operation. This paper discusses the multiphase active rectifier applications in multi-phase motor drive system. Simple hysteresis controllers are used to control the input from the source and output to the load currents (five-phase induction motor stator current). The validity of the control is validated using analytical and simulation approaches.

RNN Based Rotor Flux and Speed Estimation of Induction Motor

Speed control of induction motor can be obtained by closed loop system which require speed sensor. Speed sensor system is less effective for wide plant system, because the sensor location is too far from the main control system and measurement result is less accurate. This paper presents the development of speed sensorless field oriented control (FOC) of induction motor by using the rotor flux and speed observers. The observers only required the stator voltage and current of induction motor to obtain the rotor flux and speed estimation. The observers based on recurrent neural network (RNN) methods are implemented. Finally, the effectiveness of the proposed method is verified by simulation. Simulation results show that RNN observer can produce well the rotor flux and speed estimation. MSE values of the rotor flux estimation are between 0.000087 and 0.000264, whereas MSE values of the speed estimation are between 43.0552 and 156.0798. Keywords: field oriented control, induction motor, observer, and recurrent neural network. DOI: http://dx.doi.org/10.11591/ijpeds.v1i1.65

Filter Design for Estimating Parameters of Induction Motors With Time-Varying Loads

Periodically time-varying loads such as reciprocating compressors produce torque and speed oscillations in grid-connected induction motors. In each compression cycle, the torque and speed oscillations manifest themselves through periodic pulsations in the induction motor's stator currents. In resistance-based rotor temperature tracking and overheating monitoring, low- or bandpass filters are frequently used to estimate induction motor electrical parameters from stator currents that contain periodic pulsations. Design of such filters relies on comprehensive knowledge of the load-related periodic pulsations in the stator currents. In this paper, a narrow-band angle modulation concept is first formulated in the framework of complex vector analysis to explain the periodic pulsations in the stator current. Carson's rule is then introduced to establish an empirical relationship between the stator current's modulating frequency and the appropriate bandwidth for low- or bandpass filters. The proposed filter design rules are experimentally validated, and explanations are provided using the phase angle between the motor's complex stator current and voltage vectors.

Three-Phase Induction Motor Stator Current Optimization

This research presents two mathematical –based optimal control systems for induction motor drives leading to stator current optimization and energy saving. The first is a classical optimal control system, which uses information on torque of the motor to generate the appropriate voltage amplitude that minimizes the induction motor stator current. The second is a genetic algorithmbased optimal control system, which uses information on torque of the motor to generate the appropriate minimum stator current according to fitness function. The constant f V optimal stator current controller models have been configured and built depending on a set of experimental data using MATLAB computer program, the models were validated by simulation using a typical induction motor drive model implemented with MATLAB/Simulink toolbox. The aim of this research is the industrial machineries performance improvement by analyzing and designing the induction motor optimal stator current controller, this will minimize the stator current under different loading conditions. Depending on the motor shaft mechanical load torque in order to minimize the stator current, the controller will supply stator of motor with proper voltage and frequency using appropriate control algorithm for industrial machinery. General Terms Algorithms, Performance, Experimentation

Eigenvector/eigenvalue analysis of a 3D current referential fault detection and diagnosis of an induction motor

In this paper an integrated approach for on-line induction motor fault detection and diagnosis is presented. The need to insure a continuous and safety operation for induction motors involves preventive maintenance procedures combined with fault diagnosis techniques. The proposed approach uses an automatic three step algorithm. Firstly, the induction motor stator currents are measured which will give typical patterns that can be used to identify the fault. Secondly, the eigenvectors/eigenvalues of the 3D current referential are computed. Finally the proposed algorithm will discern if the motor is healthy or not and report the extent of the fault. Furthermore this algorithm is able to identify distinct faults (stator winding faults or broken bars). The proposed approach was experimentally implemented and its performance verified on various types of working conditions.

Online Detection of Broken Rotor Bars in Induction Motors by Wavelet Packet Decomposition and Artificial Neural Networks

We present an algorithm for the online detection of rotor bar breakage in induction motors through the use of wavelet packet decomposition (WPD) and neural networks. The system provides a feature representation of multiple frequency resolutions for faulty modes and accurately differentiates between healthy and faulty conditions, and its main applicability is to dynamic time-variant signals experienced in induction motors during run time. The algorithm analyzes rotor bar faults by WPD of the induction motor stator current. The extracted features with different frequency resolutions, together with the slip speed, are then used by a neural network trained for the detection of faults. The experimental results show that the proposed method is able to detect the faulty conditions with high accuracy.

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

Mechanical Load Fault Detection in Induction Motors by Stator Current Time-Frequency Analysis

This paper examines the detection of mechanical faults in induction motors by an original use of stator current time-frequency analysis. Mechanical faults lead generally to periodic load torque oscillations. The influence of the torque oscillations on the induction motor stator current is studied using an analytical approach. The mechanical fault results in a sinusoidal phase modulation of the stator current, which is equivalent to a time-varying frequency. Based on these assumptions, several signal processing methods suitable for stator current signature analysis are discussed: classical spectral analysis, instantaneous frequency estimation, and the Wigner distribution. Experimental and simulation results validate the theoretical approach in steady-state operating conditions

Bearing fault detection using wavelet packet transform of induction motor stator current

Induction motor vibrations, caused by bearing defects, result in the modulation of the stator current. In this research, bearing defect is detected using the stator current analysis via Meyer wavelet in the wavelet packet structure, with energy comparison as the fault index. The advantage of this method is in the detection of incipient faults. The presented method is evaluated using experimental signals. Sets of data are gathered before and after using defective bearings. Compared to conventional methods, the superiority of the proposed method is shown in the success of fault detection.

New approach for stability improvement of speed-sensorless induction-motor controls at zero frequency using multirate adaptive observer

The paper describes a new approach to improve the stability of speed-sensorless vector-controlled induction motors at zero-frequency operation. In the speed-sensorless vector control of induction motors, speed identification based on a fundamental-component model becomes unstable at zero-frequency operation. At zero frequency, the stator voltage and the stator current of induction motors are both direct current, and speed information cannot be obtained from them. The speed identification thus becomes unstable at zero frequency if disturbances such as parameter mismatches, noises and measurement errors affect the speed identification system. The paper proposes a method of improving the stability in the speed identification of speed-sensorless vector-controlled induction motors at zero frequency by means of a new configuration of the adaptive flux observer. A review of this problem is carried out, namely that speed identification becomes unstable at zero frequency, and it is shown that this problem originates in an unstable pole-zero cancellation in the speed-identification system. A new observer configuration with a multirate input control is proposed and the results of experiments carried out to confirm the effectiveness of the proposed method are given.

Induction motor stator faults diagnosis by a current concordia pattern-based fuzzy decision system

This paper deals with the problem of detection and diagnosis of induction motor faults. Using the fuzzy logic strategy, a better understanding of heuristics underlying the motor faults detection and diagnosis process can be achieved. The proposed fuzzy approach is based on the stator current Concordia patterns. Induction motor stator currents are measured, recorded, and used for Concordia patterns computation under different operating conditions, particularly for different load levels. Experimental results are presented in terms of accuracy in the detection of motor faults and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis if the input data are processed in an advantageous way, which is the case of the Concordia patterns.

Analytical closed-form solution of a space-vector modulated VSI feeding an induction motor drive

The paper presents the analysis of the time response of the stator and rotor currents in induction motor fed by space-vector pulse-width modulated voltage source inverter. This mathematical model uses the Laplace and modified Z-transform. The solution is made in two steps: (a) finding the Laplace transform of the stator voltage space vectors; and (b) finding the inverse transform of the load currents (original function) using the modified Z-transform. The solution is not dependent on the number of the pulses of the PWM pattern. All the analytical waveforms were visualized from the derived relations with the program MATHCAD. Experimental results prove the feasibility of the proposed mathematical model.

Implementation of a neural network to adaptively identify and control VSI-Fed induction motor stator currents

Implementation of a neural network to adaptively identify and control VSI-Fed induction motor stator currents

Monitoring of tool fracture in end milling using induction motor current

This paper describes the use of induction motor current to monitor tool fracture in end milling operations. The principles of induction motors are studied in this paper to establish the relationship between the motor current and the motor torque. It is shown that the square of the stator current of induction motors is approximately proportional to the motor torque. Since the occurrence of tool fracture will cause variations in the motor torque, measurement of the stator current appears to be an indirect technique for monitoring tool fracture. A sensitivity analysis of the stator current to the occurrence of tool fracture is also reported. Finally, experimental results under varying cutting conditions have been presented to demonstrate the effectiveness of this approach for the detection of tool fracture in end milling operations.