Model Of Induction Motor Research Articles

Squirrel Cage Induction Motors Accurate Modelling for Digital Twin Applications

The ongoing industrial revolution emphasizes the importance of precise machinery monitoring. Among these machines, induction motors (IMs) stand out due to their large numbers, which imply a significant part of industrial energy consumption. To achieve accurate in-service IM monitoring, robust modelling is required, with a particular emphasis on in situ constraints. In this study, we create a precise digital model for squirrel cage induction motors (SCIMs) that can be used in Industry 4.0 digital twin applications. To achieve this, we survey the existing literature, describe the main modelling techniques, identify the best models in terms of ease of implementation, and ensure the accuracy of our digital representation. We develop four methods, namely finite element analysis (FEA), thermal modelling, circuit-based models, and quantum-based fuzzy logic control, as a crucial first step in implementing digital twin (DT) technology for IMs. The quantum fuzzy logic is based on the transition from classical equations to the quantum equation determining the speed of the motor in the quantum world by passing through the Schrödinger equation. We propose the DT level of integration architecture for IMs based on the industry 4.0 reference architecture model. Finally, the main tools used to successfully implement DT for IMs are revealed.

Modeling of Induction Motor Direct Starting with and without Considering Current Displacement in Slot

This article presents a mathematical model of three-phase induction motor (IM) with a squirrel cage rotor and investigates its starting modes. Specifically, two scenarios are considered: direct starting of an IM and direct starting considering the current displacement effect in the rotor slots. Analyzing the starting modes of an IM without the use of automatic control systems is crucial for ensuring reliable, efficient, and safe operation of equipment across various industrial and commercial sectors. Understanding and accounting for the processes occurring during the starting mode of an IM allows for minimizing risks, enhancing energy efficiency, and reducing operational costs. This article details the mathematical modeling methods used for analyzing these starting modes and the results obtained from the modeling. These results were compared with data obtained experimentally, allowing for the assessment of the accuracy and reliability of the proposed model. The conducted research highlights the importance of considering current displacement in the rotor slots for accurate modeling and analysis of induction motor starting modes, particularly in capturing the differences in the amplitudes of the starting current and the faster transition to steady-state operation. Conclusions drawn from the comparison of modeling and experimental data provide valuable insights for the further development of control and operation methods for induction motors.

Mathematical modelling of induction motors taking into account design-parameter asymmetry

Due to their numerous technical and economic advantages, induction machines (IM) with squirrel-cage rotors are important components of many industrial processes. Their reliability, durability, and ease of operation make them indispensable in various industries, ensuring stable and efficient equipment performance. However, to minimise the risk of costly production failures caused by sudden stoppages, it is important to implement effective diagnostic and monitoring methods. The most common type of fault in squirrel-cage induction motors is the breakage of cage bars and end ring segments, particularly in motors with high inertia loads and frequent stops and starts. Regular maintenance and the implementation of modern control systems in the industry will help ensure the reliable and uninterrupted operation of these machines. In the technical literature, various diagnostic methods have been proposed. Some are based on analysing data collected from the induction motor (IM), detecting characteristic perturbations that indicate faults in the machine. Other methods involve comparing the data obtained from the actual induction motor with its digital model. It is clear that accurate and adequate IM models are necessary for the development and optimisation of fault diagnosis methods. This paper focuses on developing a mathematical model of an induction motor (IM) in a stator-fixed coordinate system, where the squirrel-cage rotor of the IM is represented as a multiphase, symmetrically distributed winding system in space. The adequacy of the obtained model is demonstrated. The developed model allows for the analysis of various types of asymmetries in both stator and rotor of squirrel-cage induction motors

Stator Winding Interturn Short-Circuit Fault Modeling and Detection of Squirrel-Cage Induction Motors

Stator Winding Interturn Short-Circuit Fault Modeling and Detection of Squirrel-Cage Induction Motors

Sub-Domain Model for Induction Motor With More Accurate Realization of Tooth-Saturation

Sub-Domain Model for Induction Motor With More Accurate Realization of Tooth-Saturation

State-space model for induction motors with static eccentricity faults

State-space model for induction motors with static eccentricity faults

Ways to overcome the barrier during the transition of asynchronous motors to the energy efficiency class IE5

Purpose. To provide a comprehensive analytical review on ways to increase the energy efficiency of induction motors and identify those suitable for transitioning fixed speed induction motors to IE5 level. Methodology. A thorough study of research and development in the field of increasing the energy efficiency of induction motors for various purposes was conducted. The experience of the development of traction induction motors of electric vehicles as dynamically developing is extrapolated. Results. It was established that the main limiting factor in increasing the energy efficiency of induction motors to the IE5 level is its thermal state. Analysis of ways to improve the efficiency of induction motors indicates the need for their complex application. It is most expedient to optimize and improve the elements of the air-cooling system of induction motors. To carry out the research, it is necessary to comprehensively apply electromagnetic and temperature field modeling systems of induction motors. Given certain shortcomings of thermal models of induction motors, it is necessary to combine modeling with experimental studies using both classical temperature sensors and the method of thermography. Originality. The combination of modern methods of modeling the thermal state of induction motors and experimental studies makes it possible to determine reserves for increasing the efficiency of existing motors. Using these results, it is possible to improve induction motors to the IE5 efficiency level. Practical value. Manufacturers and researchers see difficulties with the transition of induction motors of industrial purpose to the IE5 efficiency level, so they focused their attention on synchronous electric motors. The results of our research prove that, despite certain barriers, this transition can be made, albeit with certain difficulties.

ANFIS-based optimisation for achieving the maximum torque per ampere in induction motor drive with conventional PI

This research presents an innovative approach to controlling the speed of an induction motor drive by utilizing a combination of neural networks and fuzzy inference systems (ANFIS). The study focuses on computing the rotor's magnetic flux while considering different overshoot and settling criteria for torque and motor speed. The goal is to optimize torque per ampere and generate the necessary torque. The proposed ANFIS-based torque-per-ampere control technique offers a distinctive method applicable to a static induction motor model. This method allows for an increase in stator current while maintaining flexibility and individuality in motor control strategies. It compares various motor vector control methods, specifically focusing on strategies to reduce torque ripple. These strategies include adaptive ANFIS, fuzzy logic control (FLC), and proportional-integral (PI) control. The research highlights the effectiveness of an adaptive ANFIS controller in achieving the most significant reduction in torque ripple within the induction motor system. This proposed problem identification sets the stage for exploring and developing solutions to enhance the performance and efficiency of induction motor drives.

Infrared image fault diagnosis based on dual-stream attention convolution network

The development of infrared image detection technology has improved the real-time performance and safety of fault diagnosis. To address the problem of insufficient features caused by the low resolution of infrared images, a fault diagnosis model for induction motor infrared images based on Dual-Stream Attention Convolutional (DSAC) is proposed. Firstly, this model employs a dual-stream convolutional neural network to extract spatial features in the X and Y directions of the infrared images separately. This dual-stream structure allows the network to simultaneously learn and utilize information from both X and Y directions, enabling a more comprehensive capture of temperature distribution and variation trends in the infrared images. Then, a convolutional attention mechanism is introduced to assign weights to the obtained features. The attention maps generated by the convolutional attention layer enhance key features while suppressing unimportant information, thereby enhancing the focusing performance of the DSAC model on critical features. Finally, the weighted dual-stream features are fused to achieve the goal of fault diagnosis for induction motor infrared images. The DSAC model is validated using the induction motor infrared image dataset from Babol Noshirvani University of Technology, demonstrating excellent diagnostic accuracy and speed on small sample datasets, providing a feasible solution for fault diagnosis of induction motor infrared images.

The role of load and distributed energy resources modeling in voltage recovery studies

Load modeling in power systems is crucial to stability studies. The analysis of certain dynamic phenomena requires proper load modeling. Fault Induced Delayed Voltage Recovery (FIDVR) relates directly to Residential Air Conditioner (RAC) stalling. Therefore, the modeling of induction motor (IM) loads is essential to this analysis. Delayed voltage recovery in the system can result in delayed power recovery in inverters connected to the transmission system, which connects photovoltaic and wind power plants to the Brazilian Interconnected Power System (BIPS). In the last few years, there has been a considerable growth of Distributed Energy Resources (DER) in Brazil. Modern and legacy ride-through settings coexist in power systems around the world. Inverters with legacy settings are more likely to disconnect in systemic events. This creates a context that has been assessed in this paper. The modeling of induction motors in the Acre-Rondonia system leads to a deterioration of this system’s dynamic performance, which can lead to collapse. DER’s voltage support can contribute to post-fault system voltage recovery, mitigating the delay caused by RAC stalling and avoiding collapse. Thus, more robust ride-through settings are required to avoid inverter disconnection. Moreover, dynamic voltage support and reactive current priority requirements are demands from inverters connected to the system. The results obtained show that this consideration enable more consistent results than the ones obtained by the classic ZIP load model.

Algebraic Speed Estimation for Sensorless Induction Motor Control: Insights from an Electric Vehicle Drive Cycle

Induction motors (IMs) must meet high reliability and safety standards in mission-critical applications, such as electric vehicles (EVs), where sensorless control strategies are fundamental. However, sensorless rotor speed estimation demands improvements to overcome filtering distortions, tuning complexities, and sensitivity to IM model mismatch. Algebraic methods offer inherent filtering capabilities and design flexibility to address these challenges without introducing additional dynamics into the control system. The objective of this paper is to provide an algebraic estimation strategy that yields an accurate rotor speed estimate for sensorless IM control. The strategy includes an algebraic estimator with single-parameter tuning and inherent filtering action. We propose an EV case study to experimentally evaluate and compare its performance with a typical drive cycle and a dynamic torque load that emulates a small-scale EV power train. The algebraic estimator exhibited a signal-to-noise ratio (SNR) of 43 dB. The closed-loop experiment for the EV case study showed average tracking errors below 1 rad/s and similar performance compared to a well-known sensorless strategy. Our results show that the proposed algebraic estimation strategy works effectively in a nominal speed range for a practical IM sensorless application. The algebraic estimator only requires single-parameter tuning and potentially facilitates IM model updates using a resetting scheme.

Field Modeling and Thrust Analysis of Linear Induction Motor Considering Edge-End Effect and Core Saturation

Field Modeling and Thrust Analysis of Linear Induction Motor Considering Edge-End Effect and Core Saturation

Investigation of electromagnetic processes in the case of static eccentricity of a two-pole induction motor with a short-circuited rotor

Purpose. Correction of the mathematical model of electromagnetic processes in a two-pole induction motor with a short-circuited rotor, taking into account static rotor eccentricity to identify diagnostic correlations. Methodology. Analytical modeling using the method of specific magnetic conductivity, mathematical modeling of electromagnetic fields in a three-phase induction motor with a short-circuited rotor using methods of electromagnetic field theory and finite element methods. Obtained results. The necessity of improving mathematical models for induction motors with short-circuited rotors to establish new or refine connections between diagnostic features and diagnosed defects has been demonstrated. Refined mathematical expressions for calculating the specific conductivity of non-uniform air gaps in induction motors with static eccentricity are provided. Modeling was performed using the FEMM environment for a statically eccentric two-pole induction motor with a short-circuited rotor. It has been proven that the harmonic order values obtained using the numerical-field method are consistent with those obtained analytically. Findings. Based on the field approach and using the finite elements method, an analysis of the distribution of magnetic field in a two-pole induction motor with a short-circuited rotor was conducted. Harmonic analysis of the magnetic field in the air gap was performed to identify the fundamental harmonic and higher and lower-order harmonics when eccentricity occurs. The influence of static rotor eccentricity on the electromagnetic processes of the induction motor was analyzed. Practical value. The results of the study can be utilized for functional diagnosis of the rotor winding of induction motors based on the radial component of the magnetic field. This will contribute to enhancing the reliability of induction motors and enable the prevention of failure in induction motors with short-circuited rotors.

Modeling of Induction Motors and Variable Speed Drives for Multi-Domain System Simulations Using Modelica and the OpenIPSL Library

This paper introduces an innovative method for characterizing, implementing, and validating both three-phase and single-phase induction motor models, accompanied by a variable speed drive model. The primary goal is to investigate interactions between the electrical power grid and other dynamic domains (e.g., thermofluidic) that impact motor/load drive behavior. Our approach involves establishing a mechanical interface based on a physically meaningful equation linking motor torque/speed to the electrical model in the phasor domain. This allows seamless integration of diverse domain subsystems into a unified multi-domain model using Modelica v4.0.0 and the OpenIPSL library v3.0.1, overcoming co-simulation limitations. The proposed model, which requires only one Modelica-compliant tool for simulation, introduces additional dynamics through the mechanical interface, enabling explicit simulation of load disturbances based on constitutive physics. This deepens our understanding of dynamic interactions between the electrical power domain and other subsystems connected through the motor. We detail the modeled components using mathematical equations and textual descriptions, emphasizing the Modelica modeling approach. Simulation examples validate the implementation, demonstrating the multi-domain modeling capabilities of the newly developed components.

Assessment of Fractional and Integer Order Models of Induction Motor Using MATLAB/Simulink

The definition of derivatives and integrals of any real or complex order can be found in fractional calculus, which is an extension of ordinary calculus. Many real-world processes might be more accurately modeled by these fractional calculi. Flexibility and nonlocality are the two fundamental benefits of fractional derivatives. These derivatives, which are of fractional order, are more flexible than classical derivatives in how they might approach real data. Due to its applications in numerous domains, the fractional order model has grown in significance and popularity. The simulation results have been performed for three squirrel cage induction motors which have different parameter values. To perform fractional order calculus, the Fractional Order Modeling and Control (FOMCOM) toolbox has been added to MATLAB. To determine the value of the order of differentiation (α) that best represents the induction motor, speed and torque simulations for several orders of differentiation (α) were performed. According to the results of the speed and torque simulation, an integer order (α=1) model is the optimal representation of the induction motor. The main goal of this paper is to investigate which model, either integer or fractional order model, best represents an induction motor.

Robust flux angle control of induction motors to improve efficiency at light loads

AbstractVector control of induction motors has provided us with both good performance and acceptable efficiency at rated loads. Using this method at light loads reduces the efficiency due to the adjustment of motor flux at the rated value. Electric motors are normally used even at lower loads than the rated ones and often selected oversized. Enhancing the efficiency of induction motors at light loads will significantly reduce electric energy consumption. A novel method called Flux Angle Control (FAC) of the induction motor is presented to improve efficiency in the steady state at light loads. To increase the accuracy of the induction motor model we consider iron losses. This method controls the angle between the stator flux (stator current) and the d‐axis (rotor flux direction). The primary aim of the flux angle control method is to reduce the flux level and increase the flux angle at light loads. The optimal flux angle is sensitive to some induction motor parameters. The proposed method offers several advantages compared to similar methods, including high efficiency, minimal sensitivity of the optimal flux angle to changes in motor parameters, a reduced number of calculations, and easy implementation. The validity of this method is examined through experiments.

Relegated Thrust Ripples for Linear Induction Motors Based-Four Voltage Vectors Finite-Set Predictive Control and Model Reference Adaptive System

—These days, there are extensive signifies to the evolution of newfound control procedures like model predictive control. The finite-state predictive thrust control (FSPTC) for LIMs is still not deftly addressed by scholars and limited research was acted to exploit the FSPTC for LIM drive systems. Therefore, the current study proposes a novel finite-state predictive thrust control (NFSPTC) for linear induction motors (LIMs) with reduced numbers of voltage vectors (VVs) in the prediction phase. The NFSPTC utilizes the conventional direct thrust control (DTC) concept to reduce the required VVs from eight to only four which consist of two active vectors and two zero vectors. Decreasing the number of predicted VVs leads to a significant reduction in computational time. Moreover, the weighting factor is removed to reduce the effort in selecting the best weighting factor. Since the LIM drive system can be succeeded by developing a sensorless speed estimation, the linear speed is estimated depending on the model reference adaptive system (MRAS) before using actual sensors; thus, low cost can be attained. The suggested control approach is validated by simulation proofs based on a 3-kW arc machine. The gained findings clarified the effectiveness of the proposed four VVs control over the eight VVs procedure in dipping the thrust ripple. Besides, using the four VVs control under variable speed and fixed thrust load, the execution time was decreased by 50%. Furthermore, the proposed control procedures ensued in maintaining the primary flux linkage constant alongside succeeding a glowing-tracking speed profile throughout the tackled speed and thrust load variations.”

Hybrid poisson-lagrange approach based on genetic algorithm for improving performance of induction motor

ABSTRACT This article presents an intelligent approach for controlling the mechanical parameters like speed, rotor position, and torque of an induction motor. These parameters have poor performance in terms of settling time, rise time, peak overshoot, and voltage deviation. To conquer such an issue, a hybrid Poisson-Lagrange (HPL) algorithm is proposed. This algorithm is a combination of Poisson distribution and the Lagrange multiplier. It begins with the mathematical modeling of a three-phase induction motor. A multi-objective function is framed with the help of the HPL algorithm. Further, unknown variables of the multi-objective function are trained with a genetic algorithm (GA), which is coined the HPL-GA algorithm. Similar types of control for mechanical parameters are also analyzed with fuzzy logic controllers (FLC) and field-oriented controllers (FOC). It is observed that peak overshoot, rise time, settling time of speed and torque, and sensitivity are found to be minimum with HPL-GA in comparison to FLC and FOC for various loading conditions. The voltage deviation is also found to be lowest with HPL-GA with respect to others for various loading conditions and thus such an approach makes the system highly efficient and robust.

Design of Complex Vector Controller for High-Power Induction Machine Drive

In high-power drives of large commercial vehicles, the switching frequency of the inverter is always strictly limited to avoid high switching losses. In this paper, a high-performance complex vector controller in discrete domain is proposed to reduce the cross-coupling effect and improve control performance under low switching frequency. The discretization error of the induction motor model under different coordinate systems is explored, and a high-precision discrete induction motor model is proposed for the design of the complex vector controller and flux observer. In order to improve the dynamic response of the rotor flux linkage, the designed controller takes the current as the inner loop and the rotor flux linkage as the outer loop. Besides, the signal reconstruction strategy is proposed to solve the contradiction between the variable-step characteristics caused by synchronous modulation and the discretization process based on constant step size. Finally, the designed control strategy and analysis of the system are validly verified by simulation and experimental results.

Optimization of RST Controller for Speed Control of Linear Induction Motor Using Genetic Algorithm

Background: In this study, a new model of linear induction motor, along with the impact of end-effect on motor performance, is proposed. Moreover, a new strategy of control approach based on the polynomial RST controller is suggested and investigated. Objective: The proposed approach can provide a robust control strategy and overcome the limitations imposed by the proportional-integral (PI) regulator in the FOC technique. RST controller has a two-degree of freedom structure and consists of three polynomials, namely R, S and T, which are determined by the pole placement method and resolving of a Diophantine equation. Despite this, the implementation of this method is usually difficult and becomes more complicated with the complexity of the controlled plants. Methods: This study proposes the genetic algorithm (GA) optimization strategy for tuning RST controller parameters (adjust the controller coefficients) in order to achieve an adequate response time by minimizing the different objective functions, such as steady-state error, settling time, and rise time. Results: The accuracy and control performance of the proposed technique are checked and validated using Matlab /Simulink environment software tool. Simulation results reported that the proposed method (RST-GA) could provide robust solutions with perfect reference tracking and efficient disturbance rejection. Conclusion: These significant results make the proposed approach a promising technique for the design of a high-performance controller, which is highly suitable for industrial and electrical applications.