Showing papers on "Induction motor published in 2020"

DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

Abstract: Deep learning (DL) architecture, which exploits multiple hidden layers to learn hierarchical representations automatically from massive input data, presents a promising tool for characterizing fault conditions. This paper proposes a DL-based multi-signal fault diagnosis method that leverages the powerful feature learning ability of a convolutional neural network (CNN) in images. The proposed deep model is able to learn from multiple types of sensor signals simultaneously so that it can achieve robust performance and finally realize accurate induction motor fault recognition. First, the acquired sensor signals are converted to time–frequency distribution (TFD) by wavelet transform. Then, a deep CNN is applied to learning discriminative representations from the TFD images. Since then, a fully connected layer in deep architecture gives the prediction of induction motor condition based on learned features. In order to verify the effectiveness of the designed deep model, experiments are carried out on a machine fault simulator where both vibration and current signals are analyzed. Experimental results indicate that the proposed method outperforms traditional fault diagnosis methods, hence, demonstrating effectiveness in induction motor application. Compared with conventional methods that rely on delicate features extracted by experienced experts, the proposed deep model is able to automatically learn and select suitable features that contribute to accurate fault diagnosis. Compared with single-signal input, the multi-signal model has more accurate and stable performance and overcomes the overfitting problem to some degree.

Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on CRH trains.

Abstract: Stator/rotor winding faults are the common faults in squirrel-cage induction motor systems, which motivates the study of incipient fault detection and isolation (IFDI) to improve the safety and reliability of CRH (China Railway High-speed) trains. In this paper, a dynamic model for squirrel caged induction motor in d-q coordinate system is established firstly, further, the models and characteristics of incipient broken-rotor-bar fault and turn-to-turn short fault are analyzed. After that, a novel robust diagnosis design is proposed for the possible incipient stator/rotor winding faults. Experimental analyses show that the detection and isolation scheme designed in this paper provides high sensitivity and accurate isolation to incipient winding faults.

A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion

Abstract: Bearing fault diagnosis has extensively exploited vibration signals (VSs) because of their rich information about bearing health conditions. However, this approach is expensive because the measurement of VSs requires external accelerometers. Moreover, in machine systems that are inaccessible or unable to be installed in external sensors, the VS-based approach is impracticable. Otherwise, motor current signals (CSs) are easily measured by the inverters that are the available components of those systems. Therefore, the motor CS-based bearing fault diagnosis approach has attracted considerable attention from researchers. However, the performance of this approach is still not good as the VS-based approach, especially in the case of fault diagnosis for external bearings (the bearings that are installed outside of the electric motors). Accordingly, this article proposes a motor CS-based fault diagnosis method utilizing deep learning and information fusion (IF), which can be applied to external bearings in rotary machine systems. The proposed method uses raw signals from multiple phases of the motor current as direct input, and the features are extracted from the CSs of each phase. Then, each feature set is classified separately by a convolutional neural network (CNN). To enhance the classification accuracy, a novel decision-level IF technique is introduced to fuse information from all of the utilized CNNs. The problem of decision-level IF is transformed into a simple pattern classification task, which can be solved effectively by familiar supervised learning algorithms. The effectiveness of the proposed fault diagnosis method is verified through experiments carried out with actual bearing fault signals.

Driving-Cycle-Oriented Design Optimization of a Permanent Magnet Hub Motor Drive System for a Four-Wheel-Drive Electric Vehicle

Abstract: The electrical drive system is crucial to the drive performance and safety of electric vehicles (EVs). In contrast to the traditional two-wheel-driven EVs, the hub motor four-wheel-drive system can steer the vehicle by controlling the torque and speed of each wheel independently, yielding a very simple distributed drivetrain with high efficiency and reliability. This article presents a system-level design optimization method for a permanent magnet hub motor drive system for a campus patrol EV based on a practical driving cycle. An outer rotor permanent-magnet synchronous hub motor (PMSHM) and an improved model predicate current control are proposed for the drive system. Due to the lack of reducers, the direct-drive PMSHM needs to face more complex working conditions and design constraints. In the implementation, the motor design requirements are obtained through the collection of practical EV driving cycles on the campus. Based on these requirements, two models are proposed as the preliminary designs for the PMSHM. To improve their performance, an efficient multiobjective optimization method is employed to the motor considering different operational conditions. The finite-element model and thermal network model are employed to verify the performance of the optimized PMSHM. An optimal design scheme is selected by comparing the comprehensive performance of the two optimized motors. In addition, a duty-cycle model predictive current control is adopted to drive the motor. Finally, a prototype is developed and tested, and the experimental results are presented.

Multiobjective Optimization of a Double-Side Linear Vernier PM Motor Using Response Surface Method and Differential Evolution

Abstract: This paper designs a double-side linear vernier permanent-magnet motor, which incorporates the merits of high thrust force capability, high power factor, and high efficiency. Since the parameters of the motor are very sensitive and the high thrust force capability comes along with the high force ripple, a framework of multiobjective optimization is proposed to improve the overall motor performances. The sensitivity analysis is used to select significant variables and the multiobjective differential evolution with ranking-based mutation operator is conducted with the response surface models to generate the Pareto solution set. The electromagnetic performances of the optimal motor are compared with the initial motor by simulating step by step based on finite-element analysis. Finally, a prototype motor is manufactured and the experimental results validate the improved motor performances.

Induction Motors Fault Diagnosis Using Finite Element Method: A Review

Abstract: Condition monitoring and fault diagnosis of induction motors serve as essential techniques toward the reliable operation of critical industrial processes. The finite element method (FEM) offers a great insight into fundamental principle and physical operation of the machine. It can model complex magnetic circuit topology, discrete windings layouts, and nonlinear magnetic material properties of the machine. It determines the machine parameters (such as the magnetic field distribution, flux density, electromagnetic torques, and stator current) and can model localized magnetic saturation due to faults to a high degree of accuracy. Used as fault detection algorithms, the FEM can address the issues such as the lack of comprehensive fault databases through field measurements, and the difficulty in distinguishing fault severity. It can reduce the number of destructive tests required in the field/labs, simulate any faulty states of the machine. Although FEM has been widely used in induction motors’ design and analysis, its application in fault diagnosis is limited despite the promising potential. In this article, a literature review is conducted on induction motor fault diagnosis techniques using FEM. The state-of-the-art techniques reported in the literature are categorized into three streams: first, FEM-based fault diagnosis approach, second, FEM and signal processing-based approach, and third, FEM, machine learning, and other advanced techniques-based approach. The advantages of fault diagnosis techniques using the FEM are demonstrated and the future research direction is recommended.

An Online Data-Driven Method for Simultaneous Diagnosis of IGBT and Current Sensor Fault of Three-Phase PWM Inverter in Induction Motor Drives

Abstract: This article presents an online data-driven diagnosis method for multiple insulated gate bipolar transistors (IGBTs) open-circuit faults and current sensor faults in the three-phase pulsewidth modulation inverter. The fast Fourier transform (FFT) algorithm is used to extract the fault frequency spectrum features of the three-phase currents. Then, a feature selection method named ReliefF is introduced to select the most critical features by removing redundant and irrelevant features. In addition, as novel fast learning technology, a random vector functional link network is applied to learn the faulty knowledge from the historical dataset. Based on the well-learned model, the fault type and location of the converter can be accurately identified as long as the three-phase current signals are measured. Offline test results verify that the proposed method can identify the IGBT and sensor faults with an accuracy of 98.83% and outperforms the state-of-the-art learning algorithms. Moreover, the real-time hardware-in-the-loop test results show that the proposed method can successfully identify the IGBT faults and current sensor faults within 22 ms. It is robust to the dc-link voltage fluctuations, model parameters, and speed or load variations. The extensibility of the proposed method is also validated based on the test results in terms of other fault modes and drive systems.

Adopting the Topology Optimization in the Design of High-Speed Synchronous Reluctance Motors for Electric Vehicles

Abstract: This article deals with the design of high-speed synchronous reluctance motors for electric vehicle applications. The need to enhance the power density and to lower the cost leads to research on high-speed motors with a reduced amount of rare earth. Pure synchronous reluctance motors potentially operate at high speed and exhibit a cost-effective rotor compared to permanent magnets and induction motors. Nevertheless, they present reduced performances in deep flux weakening operations, in particular when the so-called radial ribs are introduced to increase the mechanical robustness of the rotor. In this article, the introduction of the radial ribs and the related design challenges are investigated and discussed. The adoption of the topology optimization tool that is able to optimize the amount, the positioning, and the sizing of suitable structural ribs is presented. A design flow integrating the topology optimization is presented. The approach leads to an original positioning of the radial ribs able to preserve the performance of the motor at high operating speed enhancing the mechanical integrity of the rotor.

Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

Abstract: In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.

Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

Abstract: Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.

Design of Passive Common-Mode Attenuation Methods for Inverter-Fed Induction Motor Drive With Reduced Common-Mode Voltage PWM Technique

Abstract: This paper investigates the influence of active zero vector pulsewidth modulation (AZPWM-1) and space vector pulsewidth modulation (SVPWM) on the design of passive common-mode (CM) attenuation methods to reduce CM current and shaft voltage in inverter-fed V/f-controlled induction motor drives. The passive CM attenuation methods examined here are the CM choke, the CM electromagnetic interference (EMI) filter, and the CM transformer. The attenuation requirement of AZPWM-1 and SVPWM is identified to design the passive CM choke and EMI filter. Based on the attenuation requirement, the design guidelines are revisited for SVPWM, and design rules are proposed for AZPWM-1. However, the CM transformer is designed based on the step change in magnitude of CM voltage of both the pulsewidth modulations (PWMs). The limitations in design, regarding switching frequency and component size for each case, are also established. It is shown that to have a similar attenuation in the considered two PWM cases, AZPWM-1 requires smaller passive components compared to SVPWM. The proposed design guidelines are substantiated with experimental results on a 1.1-kW induction motor drive.

Active Disturbance Rejection Control for Bearingless Induction Motor Based on Hyperbolic Tangent Tracking Differentiator

Abstract: To meet the operation requirements of a bearingless induction motor (BIM) control system, such as fast speed response, stable suspension, and high disturbance rejection ability, an active disturbance rejection control (ADRC) strategy based on hyperbolic tangent tracking differentiator (HTTD) is proposed. First, based on the air-gap magnetic field decoupling control of the BIM, the uncertainties of the system itself and the load disturbance are regarded as a total disturbance. The extended state observer (ESO) is used to observe the total disturbance in real time. The speed ADRC and suspension ADRC are designed for the torque winding and suspension winding, respectively. Second, the hyperbolic tangent function is applied to the tracking differentiator (TD) of ADRC to simplify its structure. The frequency characteristic curves are analyzed through the sweep-frequency test, and the design parameters are further determined. Finally, this strategy is analyzed with simulation in MATLAB/Simulink and verified on an experimental prototype. Both simulation and experimental results show that the strategy not only has good speed and current characteristics but also reduces the effect of load disturbance on the system and improves the suspension characteristics of the BIM.

A Comprehensive Comparison of Extended and Unscented Kalman Filters for Speed-Sensorless Control Applications of Induction Motors

Abstract: In this article, the real-time comparison of extended and unscented Kalman filter algorithms, which estimate the stator stationary axis components of stator currents, the stator stationary axis components of rotor fluxes, the rotor mechanical speed, and the load torque including viscous friction term, are performed under different operating conditions for speed-sensorless control applications of induction motors (IMs). Thus, it is clarified which algorithm is more suitable for state and parameter (load torque) estimation problem of IMs. For this purpose, four different real-time experimental tests have been carried out, which examine the effect of noise covariance matrices, parameter changes, sampling time, and computational burdens on estimation performance of both algorithms. Unlike the current literature, remarkable comparison results have been obtained.

Airgap Flux Based Detection and Classification of Induction Motor Rotor and Load Defects During the Starting Transient

Abstract: There has been active research on motor current signature analysis (MCSA) for over the last 30+ years, as it can provide remote monitoring of rotor and load defects using the current sensor available in the motor control center. However, many years of experience has shown that 1) rotor eccentricity and load defects cannot be distinguished and 2) false rotor cage fault indications are common in the field. There is a recent trend toward integrating “smart” self-diagnostics in electric machines through embedded sensors, especially in applications such as submersible pumps or nuclear plants where motor inspection is difficult. The objective of this article is to evaluate airgap flux measurement as an option for providing sensitive and reliable monitoring of motor and load defects. A new method based on the analysis of the airgap flux search coil voltage during motor starting is proposed for reliable detection and classification of rotor cage, eccentricity, and mechanical defects in the load. Experimental testing is provided to verify the claims made on the proposed method, for cases where MCSA fails.

Detection of Winding Asymmetries in Wound-Rotor Induction Motors via Transient Analysis of the External Magnetic Field

Abstract: Over recent decades, the detection of faults in induction motors (IMs) has been mainly focused in cage motors due to their extensive use. However, in recent years, wound-rotor motors have received special attention because of their broad use as generators in wind turbine units, as well as in some large power applications in industrial plants. Some classical approaches perform the detection of certain faults based on the fast Fourier transform analysis of the steady state current (motor current signature analysis); they have been lately complemented with new transient time–frequency-based techniques to avoid false alarms. Nonetheless, there is still a need to improve the already existing methods to overcome some of their remaining drawbacks and increase the reliability of the diagnostic. In this regard, emergent technologies are being explored, such as the analysis of stray flux at the vicinity of the motor, which has been proven to be a promising option to diagnose the motor condition. Recently, this technique has been applied to detect broken rotor bar failures and misalignments in cage motors, offering the advantage of being a noninvasive tool with simple implementation and even avoiding some drawbacks of well-established tools. However, the application of these techniques to wound rotor IMs (WRIMs) has not been studied. This article explores the analysis of the external magnetic field under the starting to detect rotor winding asymmetry defects in WRIMs by using advanced signal processing techniques. Moreover, a new fault indicator based on this quantity is introduced, comparing different levels of fault and demonstrating the potential of this technique to quantify and monitor rotor winding asymmetries in WRIMs.

Supertwisting Sliding-Mode Direct Torque and Flux Control of Induction Machine Drives

Abstract: A new supertwisting sliding-mode direct torque and flux controller (STSM-DTC) is proposed and investigated for induction motor drives. The supertwisting algorithm is a second-order sliding-mode control (SMC) that operates without high-frequency chattering and preserves the robustness properties of the classical SMC. The new STSM-DTC scheme uses torque and stator flux controllers implemented in the stator flux reference frame and it does not employ current controllers as in the conventional vector control. Both controllers contain a design parameter that allows adjusting their behavior between a linear proportional-integral (PI) like operation and a constant-gain classical sliding-mode behavior. Experimental tests show that STSM-DTC displays a very robust behavior, similar to a conventional SMC, and it works without notable steady-state chattering, as a PI controller. This article presents theoretical aspects for the new STSM-DTC scheme, stability analysis, design and implementation details, and relevant experimental results for a sensorless induction motor drive. The scheme is compared to a second-order sliding-mode controller and a linear PI controller. A robustness assessment against DTC with PI controllers, based on experimental tests, is also included.

Wavelet-based real-time stator fault detection of inverter-fed induction motor

Abstract: This work proposes a novel real-time detection scheme for incipient stator inter-turn short circuit fault in voltage sources inverter-fed induction machines. Both non-sinusoidal input voltage and the short circuit fault causes harmonics in the motor stator current and these combined harmonic components complicate the spectral analysis-based diagnosis in inverter-fed motors. Aim of the analysis is to identify the effect of inverter fundamental/switching frequency on early detection and classification of the inter-turn fault. Discrete wavelet transform based analysis is performed on stator current using daubechies1 wavelet and statistical parameter L2 norm has been computed for the detailed and approximate coefficients at different decomposition levels to obtain the most precise feature of fault. Support vector machine-based learning algorithm is used for the accurate classification of the incipient fault. The proposed method is independent of switching and fundamental frequency, the modulation index and mechanical load. Real-time detection is possible even with infinitesimal fault current of 350 mA by the proposed method. The competency of the proposed algorithm is validated using simulation and verified by hardware with VSI-fed induction motor drive.

Design, Analysis, and Impacts of Sinusoidal LC Filter on Pulsewidth Modulated Inverter Fed-Induction Motor Drive

Abstract: Squirrel-cage induction motor (SQIM) fed by a pulsewidth modulated voltage source inverter (VSI) is subjected to voltage and current surges. It may cause additional losses in the motor, insulation failure, high bearing current, and electromagnetic interference. To mitigate these problems, sinusoidal LC filter is placed at the ac-side of the VSI. This paper proposes a new methodology to design the filter. In this method, the filter inductance is chosen based on the maximum ripple in the inverter ac-side current and the voltage drop across the filter inductor, and the filter capacitor is chosen based on the steady-state reactive power demand of the SQIM. The effects of the designed filter on the switch current, losses in the drive, temperature of the dc-link capacitor, dv/dt at the motor terminal, stator flux, and air-gap torque of the SQIM are discussed. The performance of the drive with the filters, designed by the proposed method and the standard method, are experimentally tested on a two-level silicon carbide VSI-fed SQIM and comparative analysis is carried out. The impact of the designed filter on the drive is also shown when the neutral point of the filter capacitors is connected with the mid-point of the dc link. The stability of the closed-loop controlled SQIM drive with the proposed filter, and simulation results are presented to verify its performance.

Investigation of Disturbance Observers for Model Predictive Current Control in Electric Drives

Abstract: Model predictive control (MPC) of power electronic converters has obtained much attention in many applications and especially in electric motor control. As the control loop is closed by predicting the future plant behavior by means of a mathematical model, disturbances and uncertainties are important aspects when using any MPC strategy. The plant model may be inaccurate due to plenty of reasons, such as parameter mismatches or the inverter nonlinearity. If these disturbances are not properly addressed during the MPC design process, the control performance is deteriorated. Hence, a suitable disturbance observer (DOB) is required to compensate for model inaccuracies. This contribution is comparing different lumped-DOB designs in the context of a continuous-control set MPC for induction motor current control. As a baseline for comparison, a field-oriented proportional-integral (PI)-type regulator is utilized which does not require a DOB due to its integral feedback.Comprehensive experimental results prove the necessity of a proper DOB; however, it is also shown that the overall transient and steady-state control improvement due to MPC has to be bought at a high price since the computational burden is at least doubled compared to the PI-baseline.

Discrete Space Vector Modulation-Based Model Predictive Torque Control With No Suboptimization

Abstract: This article presents a simplified discrete space vector modulation (DSVM)-based predictive torque control (PTC) scheme in order to improve the performance of a two-level inverter-fed induction motor drive. DSVM technique creates a number of virtual vectors which are evaluated in the conventional all vector-based discrete space vector modulation-based model predictive torque control (DSVM-MPTC) method. The high number of admissible vectors increases the computational burden of DSVM-MPTC, significantly. In this article, an efficient optimal voltage vector selection method is proposed to reduce the computational load of DSVM-MPTC from 37 to 13 enumerations. The vector selected from the reduced set of admissible voltage vectors produces the same cost function value as that of all vector-based DSVM-MPTC in the entire range of operation of induction motor (IM) drives. The proposed method reduces the computational burden effectively without causing any suboptimization issues in both transients and steady states. Experimental results verify the effectiveness of the proposed algorithm and its superior performance compared to the switching-table-based DSVM-MPTC and the classic finite-control-set model-predictive-control which only utilizes the real voltage vectors.

Smart Sensor-Based Synergistic Analysis for Rotor Bar Fault Detection of Induction Motors

Abstract: Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. A smart sensor-based technology is proposed in this article to synergistically use vibration and current harmonics for rotor bar fault detection in IMs. The vibration signal is used for analysis of shaft speed variations and the current harmonics information is applied for rotor bar fault detection. A wireless smart sensor network is developed and used for data collection, allowing for low-cost, low space footprint, and noninvasive installation. The effectiveness of the proposed synergistic technique is examined experimentally, with results demonstrating advantages over conventional methods in terms of accurately differentiating between a healthy and faulty motor, as well as estimating the fault severity, even under zero-load IM conditions. A means to quantify the fault states as diagnostic indices is also proposed for online IM health condition monitoring.

Incipient Fault Detection in Stator Windings of an Induction Motor Using Stockwell Transform and SVM

Abstract: In this article, Stockwell transform (ST) is used to analyze the stator current signals for diagnosis of various motor conditions such as healthy, stator winding interturn shorts, and phase to ground faults. ST decomposes the current signals into complex ST matrix whose magnitude has been utilized for the fault detection. The nature of the fault, that is, ground or interturn is identified using the zero sequence currents followed by postfault detection. Two separate frequency bands are defined to extract the features which are fed to two different support vector machine (SVM) models for faulty phase detection for both types of faults. Under both cases, a heuristic feature selection approach is utilized to find the optimal features for classification purposes. Average classification accuracy of 96% has been achieved for both types of faults.

A Review on Different Aspects of Traction Motor Design for Railway Applications

Abstract: This article presents a review in three parts on the current traction motor topologies available in the railway market. In the first part, essential aspects of the electromagnetic design of railway traction motors, e.g., motor sizing and rotor configurations, are discussed. Different topologies are compared considering the wide range of applications. The pros and cons of each topology in specific applications are highlighted based on the corresponding performance requirements. In the second part, different cooling configurations of traction motors common in railway are reviewed, focusing on the solutions based on air cooling. The third part presents a review of the available insulation systems for traction motors. Two primary insulation systems used in traction applications with thermal classes of H and N are discussed. The focus in this article is placed on the main applications in railway including light rail vehicles, metros, electric multiple units, high-speed trains, and locomotives. Additionally, trends in traction motors development in the coming years are reported separately for electromagnetic, cooling, and insulation designs.

Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors.

Abstract: Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time-frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases

Feedback linearization based sensorless direct torque control using stator flux MRAS-sliding mode observer for induction motor drive.

Abstract: The high-performance Direct Torque Control (DTC) requires accurate knowledge of flux and speed information. Furthermore, the elimination of sensors leads to reduced overall cost and size of the electric drive system and subsequently improving its reliability. This paper proposes an effective sensorless direct torque control scheme for induction motor drive. The proposed scheme consists of enhancing the decoupling structure and variable estimation as well. Therefore, an enhanced direct flux and torque control based on feedback linearization is implemented in one hand. This allows obtaining a linear decoupled control together with minimized flux and torque ripples. In another hand, a combined sliding mode observer and model reference adaptive system is associated with the control scheme as sensorless algorithms for rotor speed and flux estimation. This conjunction is intended to enhance the sliding mode observer performances especially at low speed operations and reduce its sensitivity to noise and system uncertainties as well. The effectiveness of the proposed control algorithm has been verified through simulation and experimental work using MATLAB/Simulink software and dSpace 1104 implementation board respectively.

Sensorless Control of Linear Flux-Switching Permanent Magnet Motor Based on Extended Kalman Filter

Abstract: Linear flux-switching permanent magnet (LFSPM) motors have drawn extensive concern for rail transit drive systems because permanent magnets and armature windings are all located on the short primary, whereas the long secondary is made up of iron only. Thus, the LFSPM motor with low cost and high efficiency is an excellent choice for urban rail transportation system. However, the installation of linear encoders that are expensive and unreliable along with the long stator will bring about increased costs and reduced reliability. To overcome the shortcomings and fully exert the advantages of LFSPM motors, extended Kalman filter (EKF) is adopted to achieve sensorless control of LFSPMs for its better stability, robustness, and low requirements for working environment in this article. An improving tracking performance can be obtained using EKF considering the end effects of LFSPMs and the presence of noise. Both simulation and experimental results indicate that the motor can run reliably from standstill without position sensors, which is advantageous compared with other sensorless control methods based on electromotive force.

Model-Free Cooperative Adaptive Sliding-Mode-Constrained-Control for Multiple Linear Induction Traction Systems

Abstract: In order to deal with the speed cooperative control problem in the multiple linear induction traction systems consists of multiple linear induction motors, a model-free cooperative adaptive sliding-mode-constrained-control strategy is proposed considering the input magnitude and rate constraints which may cause the problem of actuator and integral saturation. First, the equivalent circuit topology of the single motor in the system is investigated. Besides, the system is considered as the multiagent system with fixed communication topology due to the interaction between adjacent motors. Then, the output observer is presented to estimate the output and the estimation algorithm of pseudo-partial derivative parameter and uncertainties is proposed. Based on the above, the proposed control scheme is presented by designing an integral sliding-mode surface containing the systematic error and an anti-windup compensator is added to eliminate the saturation. Finally, the simulations of the proposed control strategy for multiagent systems are carried out to demonstrate the effectiveness and superiority of the proposed control strategy.