Induction Motor Stator Research Articles

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

Determining Induction Motor Stator Geometric Dimensions by Image Processing and Interface Design for Motor Analysis

Determining Induction Motor Stator Geometric Dimensions by Image Processing and Interface Design for Motor Analysis

Selection of induction motor stator and rotor slot number combination for optimal NVH

With the rise of vehicle electrification, considerable research has been directed towards reducing motor noise, predominantly focusing on Permanent Magnet Synchronous Motors (PMSM). Despite the increasing popularity of the induction motor, driven by its reliability, cost-effectiveness, and lower environmental impact, discussions surrounding the Noise, Vibration, and Harshness (NVH) aspects of induction motors remain limited. This paper addresses the NVH implications associated with stator and rotor slot combinations in conventional squirrel-cage induction motors. Through a design parametric study that examines the impact of varying the number of rotor bars through analytical simulation, the findings not only identify the optimal slot combination for minimizing motor noise but also demonstrate the critical importance of selecting the proper slot configuration, as NVH performance can exhibit significant variations. A physical test was conducted to validate the analytical simulations. This research contributes valuable insights into the induction motor design, especially at the early design stage, emphasizing the necessity of considerations in achieving enhanced NVH performance.

A winding design for improving 3-phase induction motor performance

One of the most popular electric motors used today is the 3-phase induction motor, which has a sturdy design, is less expensive, and is simple to use. Improvements to the materials used in the rotor or stator of induction motors, raising the number of motor phases, and employing a 3-phase induction motor for 1-phase power are just a few of the ways the motor is now being developed to perform better. These studies are all pricey, though. The goal of this study is to determine how to enhance the motor's performance at a reasonable cost. The suggested remedy was to create a 3-phase induction motor winding with a 1-layer design that resembled a symmetrical 6-phase winding. The primary study topics were the motor's rotor speed, mechanical torque, efficiency, and winding current when it was powered by a three-phase power source. The results of the study show that, although consuming less winding current, the 3-phase induction motor with a new winding design outperforms a traditional 3-phase induction motor in terms of rotor speed, mechanical torque, and efficiency.

Practical Test on the Operation of the Three-Phase Induction Motor under Single-Phasing Fault

Single-phasing is a common problem in the three-phase electrical grid. Despite the fact that the fault occurs in one phasing, the three-phase load is affected, and therefore the load is typically turned off. The three-phase induction motor is the most commonly used in the industry; therefore, this research investigates the behavior of the three-phase induction motor under a single-phasing fault. The main aim of this paper is to answer the question, should the three-phase induction motor be turned off under a single-phasing fault? The problem is investigated theoretically and compared with practical tests to explore the parameters of the induction motor (current, stator temperature, and vibration) that are affected under healthy and single-phasing fault conditions. A practical test machine is built to test the motor behavior under single-phasing faults, where the practical experiment results are compared to those of the simulations. Despite the common recommendation under single-phasing fault is to turn off the induction motors, the preliminary results of this study show that turning off an induction motor under single-phasing can be avoided under certain operating conditions with a simple protection scheme, which is useful in some practical situations.

Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives

This paper presents a new approach for the online estimation of stator and rotor resistance of induction motors for speed sensorless vector-controlled drives, using feed-forward artificial neural networks with advanced adaptive learning rates. For the rotor resistance estimation, a neural network model based on rotor speed and stator currents is developed. The rotor flux linkages acquired from the voltage model are compared with the neural network model. The feed-forward neural network employs an adaptive learning rate as the function of the obtained error during training for quick convergence with minimal estimation error. A two-layered neural network model based on the stator voltage and current equations is developed for the stator resistance estimation. The d-q axes stator currents obtained from the developed model are compared with the acquired d-q axes stator currents. For the fast convergence with minimal estimation error, an adaptive learning rate as the function of error is adopted during training. Furthermore, the neural network estimates the induction motor’s speed. The simulation and experimental results justify that the developed algorithms track variation in the resistances quickly and precisely along with the speed as compared with the conventional constant learning rate algorithm, leading to reliable operation of the drive.

Diagnosis of fault brush winding stator induction motor using external sensor flux of three axes

This research investigates short-circuit problems in induction motor stators by constructing a motor with a changing winding and leaking flux data. The data is used to evaluate X, Y, and Z axes and map motor waves under normal and abnormal conditions. This study is expected to compare the effectiveness of the observation method from three different axes at the same motor condition to be further helpful in repairing induction motor damage caused by the circuit. From the results of the study, it shows that the harmonic value of the stator current will be more effective than the Y axis with a harmonic amplitude value of -38.8 dB, higher than the harmonic value of the current on the X axis with a difference of 33.89 dB and a difference of 0.32 dB from the Z axis. Suppose the disturbance is measured from the stray harmonic value. In that case, the flux will be more effective than the X axis with a harmonic amplitude value - 25.27 dB, higher than the harmonic value of the flux on the Y axis with a difference of 6.425 and a difference of 4.85 dB from the Z axis.

Phase-Modulus Diagram of Instantaneous Current’s Space Phasor as Diagnosis Tool of Induction Motor’s Stator Windings

The paper introduces a new approach for induction motor stator’s windings inter-turns short circuits diagnosis, based on the computer-aided monitoring of the stator Instantaneous Currents Space Phasor (ICSP). Between the stator’s windings short circuits severities and the shape of the permanent geometrical loci of ICSP, as it appears in the polar coordinates modulus-phase, are identified the appropriate correspondences which are used as on-line induction motor fault identifiers. It is shown the new short circuit identifier is insensitive to induction motor load variations. The investigative studies of this new stator windings inter-turns fault identifier are made on a simulated induction motor in Simplorer programming medium.

Reduction in induction motor heating fed by a new PWM technique: results obtained in laboratory experiments

This paper presents the laboratory results on heat produced in an induction motor stator when this is fed by a controlled inverter using a new PWM technique designed by the authors, compared to the heat produced in the same motor fed by more traditional PWM techniques.

Sequence Component Based Approach to Detect Faults in Induction Motor Stator Winding

Sequence Component Based Approach to Detect Faults in Induction Motor Stator Winding

Analytical model of dynamic system "long cable - submersible induction motor with fixed rotor" in polar coordinate system

Relevance. The stock of oil wells in the Russian Federation, where production is carried out with the help of electric centrifugal pumps or electric-driven vane pumps, is expanding every year. This trend will continue for at least a couple of decades. In its turn, the number of wells with readily available oil products in the commissioned stock is decreasing. This leads to the need to improve production quality and reduce energy consumption to maintain profitability and production volumes. This can be achieved by implementing vector energy-efficient control systems for submersible motors based on observers of induction motor state variables. In this case one should take into account transients in the supply cable of considerable length, which transmits energy from a frequency converter to a motor. The observers are based on adjustable mathematical models that describe electrical, electromechanical and mechanical processes in electric drive and related process automation systems as reliably as possible. Using complex and cumbersome adjustable mathematical models in digital signal processors due to limited performance and memory causes objective difficulties in obtaining solutions of increased accuracy in real time. On this basis, the development of an analytical model of the dynamic system "long cable – submersible induction motor with fixed rotor" in the polar coordinate system is an urgent task. Aim. Development of analytical description of the mathematical model of the induction motor with a fixed rotor in the polar coordinate system taking into account the influence of a long cable. Methods. Electric drive theory, electric machine theory, mathematical models of submersible induction motors and reduced long cable model, Vandermonde's determinant, analytical methods for solving differential equations. Result and conclusions. The authors have developed an analytical model of the regulated induction motor with a fixed rotor in the polar coordinate system taking into account the supply cable of considerable length. The developed analytical model is recommended for use in vector energy-efficient electric drive with application of phase discrimination principles taking into account the influence of processes in the supply cable line. Such electric drive has a potential for increasing the profitability of oil production from low- and medium-debt wells.

Performance Evaluation of Per-Phase Model Predictive Control Schemes for Extending Lifespan of Voltage Source Converters

Unequal thermal stress among the phase legs of a multiphase converter leads to a reduction in the useful lifespan and reliability of that converter in general. Increasing the converter’s lifespan by relieving the stressed phase leg, which suffers excessive thermal stress due to aging, is crucial. This paper evaluates two control concepts, including two per-phase model predictive control methods for extending the lifespan of a voltage source inverter. These two per-phase techniques alter the switching pattern to reduce the losses of the most aged phase leg. Hence, the loss and the corresponding thermal stress of the leg that has aged the most are reduced. In such a way, the lifespan and reliability of the converter are prolonged. Two per-phase model predictive control techniques are executed in both simulation and experiment environments, where the corresponding results are provided to evaluate the behavior of these control strategies, considering several operational aspects both in steady state and transient operation. In addition to static load conditions, two per-phase techniques are verified for the correct operation under dynamic load (induction motor) conditions.

Performance Enhancement and Assessment of the Dual Stator Induction Motor

Performance Enhancement and Assessment of the Dual Stator Induction Motor

Induction Motor Stator Winding Inter-Tern Short Circuit Fault Detection Based on Start-Up Current Envelope Energy.

Inter-turn short circuit (ITSC) is a common fault in induction motors. However, it is challenging to detect the early stage of ITSC fault. To address this issue, this paper proposes an ITSC fault detection method for three-phase induction motors based on start-up current envelope energy. This approach uses Akima interpolation to calculate the envelope of the measured start-up current of the induction motor. A Gaussian window weighting is applied to eliminate endpoint effects caused by the initial phase angle, and the enveloping energy is obtained using the energy formula as the fault feature. Finally, by combining this with the support vector machine (SVM) classification learner, fault detection of ITSC in induction motors is achieved. The experimental results show that the average accuracy of this method reaches 96.9%, which can quickly and accurately detect ITSC faults in asynchronous motors and determine the severity of the faults. Furthermore, the average accuracy of SVM in detecting early ITSC faults under no-load conditions is 98.8%, which is higher than other classification learners, including LR, KNN, and NN. This study provides a new idea for induction motor fault detection and can contribute to induction motor maintenance.

Sound Sensor Placement Strategy for Condition Monitoring of Induction Motor Bearing

Damage to the bearing elements will affect the rotation of the rotor and lead to the cessation of motor operation. Therefore, it is imperative to monitor the condition of the bearings to provide information on timely maintenance actions, improve reliability, and prevent serious damage. One of the important keys to an effective and accurate monitoring system is the placement of sensors and proper signal processing. Sound signal issued by the motor during operation capable of describing its elements’ condition. Therefore, this study aims to develop a sound sensor placement strategy appropriate for monitoring the condition of induction motor bearing components. This study was carried out on three-phase induction motors’ outer-race, inner-race, and ball-bearing sections with the signal processing method using the spectrum analysis. Furthermore, the effect of sound sensor placement on condition monitoring accuracy was determined using the One-Way Analysis of Variance (One-Way ANOVA) approach. This process tests the null hypothesis and determines whether the average of all groups is the same (H0) or different (H1). Furthermore, Tukey’s test was applied to obtain effective sound sensor placement, with voice-based condition monitoring used for effective identification. The test found that the accuracy of monitoring the bearing condition was 92.66% by placing the sound sensor at 100 cm from the motor body.

Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction Motors.

Induction motors (IMs) are widely used in industrial applications due to their advantages over other motor types. However, the efficiency and lifespan of IMs can be significantly impacted by operating conditions, especially Unbalanced Supply Voltages (USV), which are common in industrial plants. Detecting and accurately assessing the severity of USV in real-time is crucial to prevent major breakdowns and enhance reliability and safety in industrial facilities. This paper presented a reliable method for precise online detection of USV by monitoring a relevant indicator, denominated by negative voltage factor (NVF), which, in turn, is obtained using the voltage symmetrical components. On the other hand, impedance estimation proves to be fundamental to understand the behavior of motors and identify possible problems. IM impedance affects its performance, namely torque, power factor and efficiency. Furthermore, as the presence of faults or abnormalities is manifested by the modification of the IM impedance, its estimation is particularly useful in this context. This paper proposed two machine learning (ML) models, the first one estimated the IM stator phase impedance, and the second one detected USV conditions. Therefore, the first ML model was capable of estimating the IM phases impedances using just the phase currents with no need for extra sensors, as the currents were used to control the IM. The second ML model required both phase currents and voltages to estimate NVF. The proposed approach used a combination of a Regressor Decision Tree (DTR) model with the Short Time Least Squares Prony (STLSP) technique. The STLSP algorithm was used to create the datasets that will be used in the training and testing phase of the DTR model, being crucial in the creation of both features and targets. After the training phase, the STLSP technique was again used on completely new data to obtain the DTR model inputs, from which the ML models can estimate desired physical quantities (phases impedance or NVF).

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

To accurately and sensitively track the stator current of an induction motor (IM) and detect faults, stochastic resonance (SR) and the Teager energy operator (TEO) are combined to detect a fault in the residual stator current of a sliding mode observer (SMO) under strong noise interference and complex weak fault conditions. First, a new reaching law is constructed to establish an SMO for better state tracking. Second, SR is used to absorb noise and amplify the detection residuals of the SMO, and the output results are estimated by the TEO in the time domain to achieve fault detection. Finally, the detection results of the IM stator and rotor winding faults and sensor intermittent faults are presented. The experimental results show that the SMO has higher state tracking accuracy and a faster rate of convergence. Moreover, the residual of the stator current is processed by SR and the TEO, and the effectiveness of fault detection is enhanced.

An optimized stator and rotor design of squirrel cage induction motor for EMU train

This paper aims to objectively calculate the 480-kW squirrel cage induction motor (SCIM) design for the electric multiple unit (EMU) trains. This is done by optimizing the stator slot and rotor slot design to get efficiency and power factor targets. The stator slot design is achieved by limiting the width and height of the stator slot pitch according to the specified range. A depth-to-width ratio is used according to the range to optimize the design of the rotor bar slot. The design process of the induction motor consists of three steps: it determines the specification design target, calculates the specified parameters of the induction motor, and simulates the design to obtain the most optimal motor design using ANSYS Maxwell. The simulation performance values obtained an efficiency of 92.547 % and a power factor of 0.915. This value is obtained from the optimization of the rotor slot and has met the minimum requirements of efficiency and power factor in designing a SCIM. The design proposed in this paper can be developed and applied in the Indonesian domestic railway manufacturing industry.

Unraveling Induction Motor State through Thermal Imaging and Edge Processing: A Step towards Explainable Fault Diagnosis

Equipment condition monitoring is essential to maintain the reliability of the electromechanical systems. Recently topics related to fault diagnosis have attracted significant interest, rapidly evolving this research area. This study presents a non-invasive method for online state classification of a squirrel-cage induction motor. The solution utilizes thermal imaging for non-contact analysis of thermal changes in machinery. Moreover, used convolutional neural networks (CNNs) streamline extracting relevant features from data and malfunction distinction without defining strict rules. A wide range of neural networks was evaluated to explore the possibilities of the proposed approach and their outputs were verified using model interpretability methods. Besides, the top-performing architectures were optimized and deployed on resource-constrained hardware to examine the system's performance in operating conditions. Overall, the completed tests have confirmed that the proposed approach is feasible, provides accurate results, and successfully operates even when deployed on edge devices.

Use of Park’s Vector Method for Monitoring the Rotor Condition of an Induction Motor as a Part of the Built-In Diagnostic System of Electric Drives of Transport

The article is devoted to the use of Park’s vector method for operational control of the rotor condition of induction motors of traction and auxiliary drives of railway rolling stock. In the course of the analysis, it was established that in order to increase the reliability and efficiency of the operation of vehicles, it is necessary to improve and implement diagnostic systems for monitoring the current state of the most damaged elements of induction electric motors built into the drive. This paper presents the development of a new approach to monitoring the state of a squirrel-cage rotor, which is based on the use of Park’s vector approach. In the course of the research, the issue of taking into account the asymmetric power supply of the engine during the diagnostic period during industrial operation was solved, which affects the accuracy of determining the degree of damage to the rotor. On the basis of the conducted research, the algorithm of the module for diagnosing the state of the squirrel-cage rotor of an induction motor has been developed for practical use in the built-in on-board systems of vehicles, which allows us to determine the degree of damage and monitor the development of the rotor defect during operation, including in automated mode.