Showing papers on "Induction motor published in 2022"

Space Vector PWM Based DTC Scheme With Reduced Common Mode Voltage for Five-Phase Induction Motor Drive

Abstract: In multiphase induction motor drives, lowering the common-mode voltage (CMV) reduces motor insulation degradation and the existence of destructive bearing current. For this investigation, a five-phase two-level voltage source inverter (FPTL-VSI) fed five-phase induction motor (FPIM) drive is used. FPTL-VSI produces increased CMV, which cannot be totally removed. Moreover, CMV can be reduced by 80% in contrast to its peak-to-peak value with suitable selection of small and large voltage vectors in a space vector pulsewidth modulation (SVPWM) scheme. Direct torque control (DTC) combined with the SVPWM scheme can accomplish such reduced CMV performance at constant switching frequency. To provide better speed and torque control, two virtual vector (VV) based SVPWM-DTC (DTC1 and DTC2) methods are proposed in this study. Over a wide range of modulation index, the influence of each voltage VV on motor drive speed and torque response is investigated. The proposed DTC1 and DTC2 schemes are validated under steady-state and dynamic conditions over a wide range of speed fluctuations using a high-power laboratory prototype of $3.8\,\text{{k}W}$ FPIM drive. The efficacy of the proposed DTC1 and DTC2 is compared to the current literature by evaluating the effectiveness of CMV and the switching frequency.

Command Filtered Adaptive Backstepping Control for Dual-Motor Servo Systems With Torque Disturbance and Uncertainties

Abstract: In dual-motor servo systems, several factors seriously affect the tracking performance especially in high-speed and high-accuracy situations, which include machinery flexibilities, torque disturbance, unmodeled dynamics, and motor parameter differences. Given these factors, this article investigates the command filtered backstepping synchronization control (CFBSC) method for a servo system driven by two motors synchronously. Command filters are used to deal with the virtual control signals in backstepping design process to avoid the computational burden causing by repeated derivatives, and a compensation system is applied to reduce the tracking error. Adaptive control is used to compensate the torque disturbance and unmodeled dynamics. In addition, the speed and torque synchronization control signals are designed to guarantee high synchronization performance. The stability of the dual-motor servo system is proved. And the effectiveness of the proposed controller is validated through experiments.

Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation

Abstract: In the last few years, induction motor fault detection has provoked great interest among researchers because it is a fundamental element of the electric-power industry, manufacturing enterprise, and services. Hence, considerable efforts have been carried out on developing reliable, low-cost procedures for fault diagnosis in induction motors (IM) since the early detection of any failure may prevent the machine from suffering a catastrophic damage. Therefore, many methodologies based on the IM startup transient current analysis have been proposed whose major disadvantages are the high mathematical complexity and demanding computational cost for their development. In this study, a straightforward procedure was introduced for identifying and classifying faults in IM. The proposed approach is based on the analysis of the startup transient current signal through the current signal homogeneity and the fourth central moment (kurtosis) analysis. These features are used for training a feed-forward, backpropagation artificial neural network used as a classifier. From experimentally obtained results, it was demonstrated that the brought-in scheme attained high certainty in recognizing and discriminating among five induction motor conditions, i.e., a motor in good physical condition (HLT), a motor with one broken rotor bar (1BRB), a motor with two broken rotor bars (2BRB), a motor with damage on the bearing outer race (BRN), and a motor with an unbalanced mechanical load (UNB).

PLL- and FLL-Based Speed Estimation Schemes for Speed-Sensorless Control of Induction Motor Drives: Review and New Attempts

Abstract: Phase-locked loops (PLLs) and frequency-locked loops (FLLs) are of importance in power and energy applications. Both technologies have been introduced to speed-sensorless- controlled motor drives, and increasing applications of PLLs and FLLs for speed estimation are foreseen. To enable a proper and good design, a thorough review of the PLL- and FLL-based speed estimation schemes is then provided in this article. It is revealed through the review that many PLL- and FLL-based estimation schemes fail to accurately track a frequency ramp (i.e., obvious estimation errors appear), which may lead to a compromised estimation accuracy when these schemes that are applied in induction motor drives operating during acceleration and deceleration processes. To address this, the proven speed estimation schemes together with new attempts are also presented in this article. Moreover, various challenges to the PLL- and FLL-based speed estimation schemes, including harmonics, dc offsets, and parameter variations, are considered when evaluating these schemes. Solutions to tackle these disturbances are accordingly presented. In addition, two representative estimation schemes are exemplified through experimental tests. Finally, further challenges in using the PLL- and FLL-based schemes for speed estimation are discussed.

Improved DTC Technique for THL-NPC VSI Fed Five-Phase Induction Motor Drive Based on VVs Assessment Over a Wide Speed Range

Abstract: A three-level neutral point clamped voltage source inverter fed five-phase induction motor (FPIM) drive is assessed by analyzing common-mode voltage (CMV). The presence of CMV leads to motor bearing fault and phase winding insulation failure due to additional voltage stress. To nullify the effect of CMV, this article proposes a modified direct torque control (DTC) based on the virtual vector (VV) concept. The proposed DTC utilizes the appropriate nonvertex voltage vectors to form VVs that neutralize the average volt-second in the $xy$ plane, maintain dc-link capacitor voltage balancing, and limits the switching voltage stress. The theoretical analysis is carried out to investigate the VVs effect on change in flux and torque response. Based on this assessment, a modified lookup table is designed for a wide range of speed operations. For the low-speed operation, i.e., below 25% of rated speed, the small VVs provide better drive performance instead of zero VV. The proposed DTC method is verified at steady-state and dynamic conditions through experimental investigations. Comparative results are provided for the assessment of the proposed DTC method in comparison to existed methods. It is concluded from the findings that the proposed DTC method is a promising approach to improving FPIM performance at high- and low-speed operations with zero CMV output.

Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review

Abstract: Magnetic flux analysis is a condition monitoring technique that is drawing the interest of many researchers and motor manufacturers. The great enhancements and reduction in the costs and dimensions of the required sensors, the development of advanced signal processing techniques that are suitable for flux data analysis, along with other inherent advantages provided by this technology, are relevant aspects that have allowed the proliferation of flux-based techniques. This article reviews the most recent scientific contributions related to the development and application of flux-based methods for the monitoring of rotating electric machines. Particularly, aspects related to the main sensors used to acquire magnetic flux signals as well as the leading signal processing and classification techniques are commented on. The discussion is focused on the diagnosis of different types of faults in the most common rotating electric machines used in industry, namely: squirrel cage induction machines, wound rotor induction machines, permanent magnet machines, and wound field synchronous machines. A critical insight of the techniques developed in the area is provided and several open challenges are also discussed.

Enhancement of the Direct Torque Control by using Artificial Neuron Network for a Doubly Fed Induction Motor

Abstract: Direct Torque Control (DTC) is the most popular strategy used in the industrial sector, because of its various advantages, however, the torque ripples makes it less efficient, due to the use of the hysteresis comparators, leading to variable frequency operation and on the other hand, the finite frequency sampling results in a pseudo-random overshoot of the hysteresis band, Thus, operation at low speed and in particular, with variations in motor resistances, affects the behavior of the machine, in this reason, this article presents a new study to promote its drawbacks to increase the control performances. A new intelligent direct torque control applied to a Doubly Fed Induction Motor (DFIM) by two Vector Source Inverters (VSIs) based on an Artificial Neuron Network (ANN) who will replace the speed controller, switching tables, and hysteresis comparators, with this special technique simulated in Matlab/Simulink, approved several improvements on motor and control behaviors so as, the torque ripples has been improved by 55.82 %, the overshoot is absolutely removed and increasing important values of total harmonic distortion (THD) by 3.26 % and 3.31 % for stator and rotor currents respectively.

Space Vector PWM Based DTC Scheme With Reduced Common Mode Voltage for Five-Phase Induction Motor Drive

Abstract: In multiphase induction motor drives, lowering the common-mode voltage (CMV) reduces motor insulation degradation and the existence of destructive bearing current. For this investigation, a five-phase two-level voltage source inverter (FPTL-VSI) fed five-phase induction motor (FPIM) drive is used. FPTL-VSI produces increased CMV, which cannot be totally removed. Moreover, CMV can be reduced by 80% in contrast to its peak-to-peak value with suitable selection of small and large voltage vectors in a space vector pulsewidth modulation (SVPWM) scheme. Direct torque control (DTC) combined with the SVPWM scheme can accomplish such reduced CMV performance at constant switching frequency. To provide better speed and torque control, two virtual vector (VV) based SVPWM-DTC (DTC1 and DTC2) methods are proposed in this study. Over a wide range of modulation index, the influence of each voltage VV on motor drive speed and torque response is investigated. The proposed DTC1 and DTC2 schemes are validated under steady-state and dynamic conditions over a wide range of speed fluctuations using a high-power laboratory prototype of $3.8\,\text{{k}W}$ FPIM drive. The efficacy of the proposed DTC1 and DTC2 is compared to the current literature by evaluating the effectiveness of CMV and the switching frequency.

Freewheeling Current-Based Sensorless Field-Oriented Control of Five-Phase Permanent Magnet Synchronous Motors Under Insulated Gate Bipolar Transistor Failures of a Single Phase

Abstract: Model-based sensorless field-oriented control (FOC) suffers from overparameterization and can be laborious to use for a five-phase permanent magnet synchronous motor On the other hand, insulated gate bipolar transistor (IGBT) frequently fails in an electric drive Under IGBT failure, a freewheeling current is observed, and, above all, it carries the failed phase back electromotive force information Based on this observation, this article presents the design of a brand new sensorless FOC by exploiting the freewheeling current to accommodate both IGBT and position sensor failures, which is expected to further enhance the drive's fault-tolerant capability The mathematical model of this current is first established to provide a theoretical basis and a comprehensive understanding of the presented sensorless FOC By virtue of this model, a second-order generalized integrator with a frequency-locked loop can be used as a simple and elegant way to extract position/speed estimates Experimental results are provided to validate the proposed sensorless FOC philosophy

A Novel Neuro Fuzzy Method DTC of Induction Motor Drive for Efficient Torque Response

Abstract: It is proposed in this paper that a modified Neuro Fuzzy (NF) approach dependant Direct Torque Controller (DTC) model be used for a non-linear system such as an Induction Machine Drive (IMD). There has been consideration of a straightforward tuning strategy that significantly reduces the variance between real and necessary acceleration of the drive by altering the settings of the succeeding levels of the NF Controller. Traditional Voltage Source Inverter (VSI) fed DTC models display significant ripple content because to the set magnitude limitations of flux and torque hysteresis controllers, which are supplied by the VSI. The hysteresis band limitations are subjected to an online tuning method in order to eliminate such irregularities in terms of torque changes. Aside from that, the suggested controller has been successfully developed for three and five-level VSI using the MATLAB/SIMULINK tool, and simulations have been conducted under a variety of operating situations. Finally, when compared to the traditional PID controller, the findings show that the torque ripple has been minimised.

Direct Torque Control With Constant Switching Frequency for Three-to-Five Phase Direct Matrix Converter Fed Five-Phase Induction Motor Drive

Abstract: For heavy industrial drive applications, the direct matrix converter (DMC) solves the problems caused by the two-stage power conversion mechanism. Due to a lack of technological advancement, the control of multiphase drives operated by DMC is a significant cause of concern. This article thus presents a direct torque control (DTC) scheme based on space vector pulsewidth modulation (SVPWM) for a five-phase induction motor (FPIM) driven by a three-to-five ( $3\times 5$ ) phase DMC. This proposed SVPWM-DTC employs the virtual vector (VV) concept to eliminate the effect of the $xy$ component on $3\times 5$ DMC output voltage space vectors. A novel approach is applied to analyze the effect of SVPWM-VV on the stator flux, torque, and speed of FPIM drive. Additionally, this SVPWM-VV regulates the input power factor of $3\times 5$ DMC. The proposed work is simulated first and further validated by $3\times 5$ DMC fed FPIM hardware prototype using field programmable gate array (FPGA)-based controller.

Predictive Control With Battery Power Sharing Scheme for Dual Open-End-Winding Induction Motor Based Four-Wheel Drive Electric Vehicle

Abstract: For high-power electric vehicles (EVs), the drive propulsion based on induction motors is emerging as economical alternative. Compared to conventional induction motors, the open-end winding induction motor (OEWIM) requires only half the dc-bus voltage for the given torque. The EV power train based on the dual two-level voltage-source inverter (VSI)-fed OEWIM with isolated dc sources is used in this research. For uniform state-of-charge (SoC) distribution, the power flow from each isolated source needs to be controlled. A two-stage model-predictive direct torque control (MPDTC) scheme is proposed to balance the SoC of batteries by proper selection of the VSI voltage vectors. The proposed MPDTC scheme is free from weighting factor tuning and uses a ranking method to predict the optimal voltage vectors. The superiority of the proposed controller in terms of battery SoC balancing is demonstrated. The performance of the proposed MPDTC EV drive is verified for the FTP75 and HFET driving cycles under different operating conditions, both by simulation and hardware experimental tests.

Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors

Abstract: The incorporation of new technologies as training methods, such as virtual reality (VR), facilitates instruction when compared to traditional approaches, which have shown strong limitations in their ability to engage young students who have grown up in the smartphone culture of continuous entertainment. Moreover, not all educational centers or organizations are able to incorporate specialized labs or equipment for training and instruction. Using VR applications, it is possible to reproduce training programs with a high rate of similarity to real programs, filling the gap in traditional training. In addition, it reduces unnecessary investment and prevents economic losses, avoiding unnecessary damage to laboratory equipment. The contribution of this work focuses on the development of a VR-based teaching and training application for the condition-based maintenance of induction motors. The novelty of this research relies mainly on the use of natural interactions with the VR environment and the design’s optimization of the VR application in terms of the proposed teaching topics. The application is comprised of two training modules. The first module is focused on the main components of induction motors, the assembly of workbenches and familiarization with induction motor components. The second module employs motor current signature analysis (MCSA) to detect induction motor failures, such as broken rotor bars, misalignments, unbalances, and gradual wear on gear case teeth. Finally, the usability of this VR tool has been validated with both graduate and undergraduate students, assuring the suitability of this tool for: (1) learning basic knowledge and (2) training in practical skills related to the condition-based maintenance of induction motors.

Postfault Direct Field-Oriented Control of Induction Motor Drive Using Adaptive Virtual Current Sensor

Abstract: Electric drives immune to failures of selected elements of the power system or measuring sensors have been the subject of research in recent years, due to the growing interest in systems with an increased level of safety. This article concerns the analysis of the direct field-oriented control induction motor drive after the failure of all stator current sensors. An adaptive virtual current sensor (AVCS) was used to estimate the stator current, based on the measurement of the motor angular velocity and the voltage in the $\text{DC}\ (u_{\text{DC}})$ of the voltage-source inverter. This article presents the possibility of improving the accuracy of stator current estimation using the original approximation of changes in rotor resistance depending on the drive operating point when the measurement information about the stator current has been lost. This approximation was determined experimentally during the normal operation of the drive using a rotor resistance estimator. The determined approximate lookup functions were used to adapt the rotor resistance in the AVCS during the drive system operation in a postfault mode. This approach has made it possible to significantly improve the accuracy of the stator current reconstruction, especially under low speeds and load torques, which was demonstrated in experimental studies .

Diagnostic of Combined Mechanical and Electrical Faults in ASD-Powered Induction Motor Using MODWT and a Lightweight 1-D CNN

Abstract: The early fault detection in the rotary electrical machines,such as induction motors (IMs), has been growing in modern industry. IMs have been widely used in industrial applications due to its easy installation, reliability, and low cost. However, the increasing usage of IMs also increases the need for timely maintenance in order to ensure their operation and a longer service life. This article proposes a new diagnosis methodology based on maximal overlap discrete wavelet transform and a lightweight 1-D convolutional neural network (CNN) architecture, in order to detect mechanical and electrical faults and their combination, in adjustable speed drive (ASD)-powered IMs. Specifically, single and combined faults were studied from the next: Outer raceway bearing (mechanical), turn-to-turn short-circuit, and phase-to-ground short circuit (electrical). The presented study was developed using current signals acquired from stators of IMs of 1 hp. The current signals are measured at powered conditions introduced by a power grid with a constant frequency at 60 Hz, and an ASD at three different frequencies. The proposed diagnostic methodology reaches more than 99% of accuracy.

Model Predictive Current Control of Six-Phase Induction Motor Drives Using Virtual Vectors and Space Vector Modulation

Abstract: The use of multiphase machines has become a suitable choice in high-performance industry applications through advantages such as lesser torque ripple, enhanced current distribution per phase, and fault-tolerance capability. Among different control approaches for the regulation of multiphase drives, model-based predictive current control (MPCC) is one of the most analyzed strategies due to its adaptability and good dynamic response. However, this approach presents some disadvantages, e.g., high $x-y$ currents and increased harmonic content in the fundamental $\alpha -\beta$ stator currents. Modulation strategies have been combined with MPCC to overcome these shortcomings. This article proposes a modulated MPCC with virtual vectors and space vector modulation for the regulation of an asymmetrical six-phase induction machine to minimize the $x-y$ currents, reduce the harmonic content, and perform improved stator currents tracking compared with other MPCC versions. Experimental tests are provided to demonstrate the quality of the proposed current control strategy.

Diagnosis and Fault Detection of Rotor Bars in Squirrel Cage Induction Motors Using Combined Park’s Vector and Extended Park’s Vector Approaches

Abstract: The induction motor (IM) is considered to be one of the most important types of motors used in industries. A sudden failure in this machine can lead to unwanted downtime, with consequences in costs, product quality, and safety. Over the last decade, several methods and techniques have been proposed to diagnose and detect faults in induction machines. In this paper, we present the development of a new algorithm based on the combination of both the Park’s vector approach (PVA) and the extended Park’s vector approach (EPVA) for broken rotor bars (BRBs) fault detection and identification. This fault can be detected using the PVA by monitoring the thickness and orientation of the park’s vector pattern and using EPVA by identifying specific spectral components related to the fault. For ability evaluation of our suggested algorithm, simulations and experiments are conducted and presented. The obtained results demonstrate that the proposed algorithm is accurate and effective and can be extensively used in IM fault detections and identifications.

Reactive-Power-Based Robust MTPA Control for v/f Scalar-Controlled Induction Motor Drives

Abstract: This article proposes a new maximum torque per ampere (MTPA) control strategy for an induction motor (IM) controlled by the scalar ( v / f ) method. In scalar control, constant air-gap flux control is generally adopted for IM drives to provide the same torque capability in all operating ranges. However, this control method results in poor motor efficiency due to excessive stator current at light loads. This work proposes a new MTPA control method to minimize the stator current amplitude based on reactive power without measuring the rotor mechanical speed, resulting in improved system efficiency at light loads. In addition, this method proposes an online machine parameter compensation method. Thus, this proposed method is robust to parameter variations. Simulation and experimental results are provided to verify the performance of this MTPA control strategy.

An Adaptive Active Disturbance Rejection Control Strategy for Speed-Sensorless Induction Motor Drives

Abstract: Speed-sensorless control of induction motor drives based on the extended state observer (ESO) attracts much popularity due to its key characteristics, e.g., satisfactory estimation performance, and high robustness against disturbances. However, the conventional ESO generally uses fixed high gains to achieve fast convergence. This may bring about the concern of noise sensitivity. Moreover, a proportional-integral (PI)-type speed controller is mostly adopted in speed-sensorless control of induction motor drives, which may cause unsatisfactory system dynamics. To address these, an adaptive active disturbance rejection control (ADRC) strategy is proposed for speed-sensorless induction motor drives in this article. In this scheme, a third-order adaptive ESO (AESO) is first used to estimate the speed, the phase, and the overall disturbance. Then, a speed controller based on the state error feedback control rate (SEFCR) is designed to improve system dynamics. In practice, disturbances like dc offsets may further challenge estimation performance, and hence, a closed-loop flux observer (CLFO) is employed to deal with this issue. Additionally, a parameter sensitivity analysis of the CLFO is also provided to evaluate the performance of the proposed scheme. Finally, extensive experimental tests have been carried out to validate the effectiveness of the proposed.

Hybrid Optimization Approach for Energy Control in Electric Vehicle Controller for Regulation of Three-Phase Induction Motors

Abstract: Three-phase induction motors are becoming increasingly popular for electric cars and industrial uses because of their improved efficiency and simplicity of production, among other things. Many enterprises and industries use induction motors in several rotating applications. However, it is a difficult talent to master when it comes to controlling the speed of an induction motor for various purposes. This study examines the performance of a three-phase induction motor using approaches such as field-oriented control and direct torque control. This work utilized the fractional order Darwinian particle swarm optimization (FODPSO) method in fuzzy methodology to optimize a motor’s performance. Field-oriented control (FOC) and Direct torque control (DTC) methods are regulated by FODPSO, which is compared to standard FOC and DTC methods. MATLAB-Simulink was used to compare the outcomes of each system’s simulation model to determine which one performed the best. The support vector machine-direct torque control (SVM-DTC) technology is famous for its rapid dynamic response and decreased torque ripples. Using torque and settling time and rising time reduction, the suggested technique is proved to be superior to the present way.

Analysis of Control Methods for the Traction Drive of an Alternating Current Electric Locomotive

Abstract: The analysis of operating conditions of traction drives of electric locomotives with asynchronous traction motors has been carried out. It was found that during operation in the output converter of an asynchronous motor, defects may occur, which leads to asymmetric modes of its operation. Models of a traction drive of an electric locomotive with asynchronous motors with scalar and vector control of the output converter are proposed, taking into account asymmetric operating modes. As a result of the simulation, the starting characteristics of the traction drive were obtained for various control methods both in normal and emergency modes of the drive. For the drive-in emergency mode, the following cases were investigated: the balance of the converter output voltages and the turn-to-turn circuit of 10% of phase A winding of the motor stator; imbalance of the output voltages of the inverter and an intact motor; imbalance of the output voltages of the converter and interturn short circuit of 10% of phase A winding of the motor stator. Comparison of the simulation results have shown that in emergency modes in the traction drive, the torque ripple on the motor shaft in the drive with vector control is 13% less, and in scalar control, the phase current unbalance coefficient is 22% less. The results of this work can be used to study the influence of the output converter control methods on the energy efficiency indicators of the traction drive of an AC electric locomotive.

Integral Sliding Mode Observer-Based Ultralocal Model for Finite-Set Model Predictive Current Control of Induction Motor

Abstract: The ultralocal model of a plant expresses the behavior of the output variable in relation to the input without requiring any specific information about the plant. Replacing the conventional model of the system with an ultralocal model can effectively improve the robustness of the model predictive control (MPC) because all types of uncertainties that correlate with the system modeling are removed in the ultralocal model. This article proposes an integral sliding mode observer (ISMO)-based ultralocal model for the finite-set model predictive current control (FS-MPCC) of an induction motor (IM). The ISMO is constructed based on the Lyapunov theory to guarantee the stability of the proposed control method. Moreover, an analytical comparison is made between the proposed ISMO and the conventional extended state observer (ESO) in the application of the ultralocal model. This comparison shows that the stability of ESO depends on the unknown term of the ultralocal model. In contrast, the stability of the proposed ISMO does not rely on any terms of the ultralocal model. Experimental tests confirm that the proposed FS-MPCC method is practically applicable to the IM drive system. Also, it improves the robustness of the predictive method against model uncertainty.

New Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection

Abstract: Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology for induction-motor fault detection. Some of the motor parameters such as the stator currents and vibration signals provide a great deal of information about the motor’s conditions. Therefore, these signals of the motor were selected to test the proposed model. The induction motor was assessed in a laboratory under healthy, mechanical, and electrical faults with different loadings. In this study a new hybrid model was developed using the collected signals, an optimal features selection mechanism is proposed, and machine learning classifiers were trained for fault classification. The procedure is to extract some statistical features from the raw signal using Matching Pursuit (MP) and Discrete Wavelet Transform (DWT). Then, the Invasive Weed Optimization algorithm (IWO)-based optimal subset was selected to reduce the data dimension and increase the average accuracy of the model. The optimal subset of features was fed into three classification algorithms: k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), which were trained using k-fold cross-validation to distinguish between the induction motor faults. A similar strategy was performed by applying the Genetic Algorithm (GA) to compare with the performance of the proposed method. The suggested fault detection model’s performance was evaluated by calculating the Receiver Operation Characteristic (ROC) curve, Specificity, Accuracy, Precision, Recall, and F1 score. The experimental results have proved the superiority of IWO for selecting the discriminant features, which has achieved more than 99.7% accuracy. The proposed hybrid model has successfully proved its robustness for diagnosing the faults under different load conditions.

A novel starting method with reactive power compensation for induction motors

Abstract: This paper investigates several traditional startup methods for induction motors. Since a large starting current and a reactive power may lead to a deep voltage drop and cause a potential damage to induction motors and other devices in the same power grid, a novel starting method is proposed for induction motors based on the autotransformer and the magnetically controlled reactor (ATMCR). Then, a reactive power dynamical compensation strategy is developed to improve the power factor and overcome the voltage drop effectively. The main advantage of the proposed approach is that the starting current of induction motors is reduced in the starting period and compensate the reactive power simultaneously. In simulation, a parameter identification method is presented to deal with simulation parameters and the motor's nameplate parameters inconsistencies problem. Finally, simulation and experiment are provided to verify the effectiveness of the proposed approaches.

Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition

Abstract: When an induction motor is running at stable speed and low slip, the fault signal of the induction motor’s broken bar faults are easily submerged by the power frequency (50 Hz) signal. Thus, it is difficult to extract fault characteristics. The left-side harmonic component representing the fault characteristics can be distinguished from power frequency owing to V-shaped trajectory of the fault component in time-frequency (t-f) domain during motor startup. This article proposed a scheme to detect broken bar faults and discriminate the severity of faults under starting conditions. In this scheme, successive variable mode decomposition (SVMD) is applied to analyze the stator starting current to extract the fault component, and the signal reconstruction is proposed to maximize the energy of the fault component. Then, the quadratic regression curve method of instantaneous frequency square value of the fault component is utilized to discriminate whether the fault occurs. In addition, according to the feature that the energy of the fault component increases with the fault severity, the energy of the right part of the fault component is proposed to detect the severity of the fault. In this paper, experiments are carried out based on a 5.5 kW three-pole induction motor. The results show that the scheme proposed in this paper can diagnose the broken bar faults and determine the severity of the fault.

Dilated convolutional neural network based model for bearing faults and broken rotor bar detection in squirrel cage induction motors

Abstract: Deep learning can play a pivotal role in early fault detection in squirrel cage induction motors (SCIMs) and achieving Industry 4.0. SCIM finds application in industries like mining, textile, manufacturing, and many more. Early fault detection in SCIM can significantly reduce downtime and optimize productivity. This paper proposes a novel fault detection technique for bearing faults and broken rotor bar detection in SCIM using the dilated convolutional neural network-based model. A simple 1-D signal to image conversion technique is also proposed for transforming the 1-D vibration signal acquired from multiple accelerometers to images. The proposed method provides an end-to-end learning solution for fault detection. The propounded approach has accomplished an average accuracy of more than 99.50%. A comparison has also been made between different convolutional neural network (CNN) models and conventional machine learning models to show the proposed method’s efficiency. The complete experimental work has been carried out on a 5 kW, 3-phase, 415 V, 50 Hz SCIM. The dilated CNN model development has been done using python software, and the packages used are Keras and TensorFlow.