Traction Induction Motor Research Articles

Increasing the efficiency of railway rolling stock operation with induction traction motors due to implementation of the operational system for diagnostic condition of rotor

Increasing the efficiency of railway rolling stock operation with induction traction motors due to implementation of the operational system for diagnostic condition of rotor

Magnetizing Inductance Estimation Method of Induction Motor for EV Traction Considering Magnetic Saturation Changes According to Current and Slip Frequency

Magnetizing Inductance Estimation Method of Induction Motor for EV Traction Considering Magnetic Saturation Changes According to Current and Slip Frequency

Lyapunov-Based Finite Control Set Applied to an Induction Motor for Agricultural Electrical Traction

Lyapunov-Based Finite Control Set Applied to an Induction Motor for Agricultural Electrical Traction

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.

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

Maintaining optimal temperatures in the critical parts of an induction traction motor is crucial for railway propulsion systems. A reduced-order lumped-parameter thermal network (LPTN) model enables computably inexpensive, accurate temperature estimation; however, it requires empirically based parameter estimation exercises. The calibration process is typically performed in labs in a controlled experimental setting, which is associated with a lot of supervised human efforts. However, the exploration of machine learning (ML) techniques in varied domains has enabled the model parameterization in the drive system outside the laboratory settings. This paper presents an innovative use of a multi-agent reinforcement learning (MARL) approach for the parametrization of an LPTN model. First, a set of reinforcement learning agents are trained to estimate the optimized thermal parameters using the simulated data in several driving cycles (DCs). The selection of a reinforcement learning agent and the level of neurons in the RL model is made based on variability of the driving cycle data. Furthermore, transfer learning is performed on a new driving cycle data collected on the measurement setup. Statistical analysis and clustering techniques are proposed for the selection of an RL agent that has been pre-trained on the historical data. It is established that by synergizing within reinforcement learning techniques, it is possible to refine and adjust the RL learning models to effectively capture the complexities of thermal dynamics. The proposed MARL framework shows its capability to accurately reflect the motor’s thermal behavior under various driving conditions. The transfer learning usage in the proposed approach could yield significant improvement in the accuracy of temperature prediction in the new driving cycles data. This approach is proposed with the aim of developing more adaptive and efficient thermal management strategies for railway propulsion systems.

Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest

The stator winding insulation system is the most critical and weakest part of the EMU’s (electric multiple unit’s) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent a fault from expanding into phase-to-phase or ground short-circuits. The TCU (traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is at a standstill. Three ITSC fault diagnostic conditions are based on different IGBTs’ control logics. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on the random forest. The proposed method was validated using a simulation experimental platform for the ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with an increase in the ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location, both in train and test samples, is 100%.

Modeling of energy-efficient direct torque control system for traction induction motor

A brief analysis of existing control systems for traction electric drives with induction motors has been carried out, as a result of which it has been established that vector control and direct torque control systems are the most promising. The comparative characteristic of these control systems according to different parameters of electric drive quality has shown that in energy-saving algorithms it is appropriate to use the systems of direct torque control. The work aims to simulate the electric drive with a direct torque control system, which provides the reduction of power losses in an asynchronous motor. The value of the stator winding power factor is chosen as a control action. The calculation and functional dependences between the main parameters of the control system and the induction motor are synthesized, the fulfillment of which allows reducing duce power losses. The functional diagram of the electric drive with an energy-efficient control system is given. The hardware of this control system does not differ from the currently used frequency converters. In the power channel of the frequency converter, a three-level autonomous voltage inverter with locking diodes is used. The results of modeling for electric drive with 11 kW motor, analysis of which showed the adequacy of the developed model and performance of the synthesized control system, the efficiency of the electric drive with energy-efficient control system increases to 18% compared with the classical direct torque control system.

Sliding mode control rotor flux MRAS based speed sensorless induction motor traction drive control for electric vehicles

<abstract><p>Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R & D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PI-MRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics.</p></abstract>

Thermal Model of the Output Traction Converter of an Electric Locomotive with Induction Motors

The aim of research is to develop a thermal model of the output converter of an electric locomotive with vector control, taking into account the operating modes of the electric locomotive. The aim was achieved by using energy losses at the moments of switching functions that describe the nature of the change in the curves of currents and power voltages of transistors and converter diodes. The object of research is an autonomous voltage converter, which is part of the traction drive of an electric locomotive with a vector control system. The simulation was carried out for an autonomous voltage converter, the load of which is two traction induction motors operating in the nominal mode. The temperature of transistors and diodes is obtained for the established modes of operation of traction motors. The most important results are the analytical dependence of thermal power losses in the inverter as a function of inverter phase currents and voltages. The significance of the results obtained is to establish the dependence of the temperature of the power devices of the inverter on the actual operating conditions of the electric locomotive. The established dependencies will further determine the spectral power of thermal noise. This will allow, when designing a traction drive control system, to develop an effective system for filtering thermal noise. This will increase the energy performance of the traction drive of the electric locomotive by improving the accuracy of regulation.

Analysis of Test Results of the Developed Synchronous Reluctance Motor for Public Transport Application

Abstract The paper describes the design and control method of the synchronous reluctance machine with improved efficiency compared to traction induction motor for electric vehicle application. Magnetic field finite element modelling is used in the design process. The paper presents load characteristics calculation method for the design process, considering the cross magnetic saturation effect. Control algorithm with constant d-axis current control method is developed in the research. The prototype of the machine is constructed and tested.

Mitigation of Circulating Bearing Current in Induction Motor Drive Using Modified ANN Based MRAS for Traction Application

Induction motors are popularly used in various applications because of the proposed modest construction, substantiated process, and limited size of specific power. The traditional AC traction drives are experimentally analyzed. There is a high circulating current due to the high Common-Mode Voltage (CMV). The high Circulating Bearing Current (CBC) is a major problem in conventional two-level voltage source inverter fed parallel-connected sensor-based induction motors for traction applications. A sensorless method is well known for shrinking costs and enhancing the reliability of an induction motor drive. The modified artificial neural network-based model reference adaptive system is designed to realize speed estimation methods for the sensorless drive. Four dissimilar multilevel inverter network topologies are being implemented to reduce CBC in the proposed sensorless traction motor drives. The multilevel inverter types are T-bridge, Neutral Point Clamped Inverter (NPC), cascaded H-bridge, and modified reduced switch topologies. The four methods are compared, and the best method has been identified in terms of 80% less CMV compared to the conventional one. The modified cascaded H-bridge inverter reduces the CBC of the proposed artificial neural network-based parallel connected induction motor; it is 50% compared to the conventional method. The CBC of the modified method is analyzed and associated with the traditional method. Finally, the parallel-connected induction motor traction drive hardware is implemented, and the performance is analyzed.

Application of Digital-Twin Technology in Developing Traction Induction Motors

Application of Digital-Twin Technology in Developing Traction Induction Motors

Application of digital twin technology in the development of traction induction motors

Application of digital twin technology in the development of traction induction motors

Моделирование режимов тягового вентильно-индукторного электропривода троллейбуса

The results from development and simulation of a switched reluctance motor for a trolleybus traction drive are discussed. The project is based on the motor modular designing principles, and the 180 kW TAD-3 induction motor with which trolleybuses are currently equipped serves as a prototype. The switched reluctance motor’s parameters were obtained from a numerical analysis of the magnetic field using the ELCUT computer program and have been embedded into the MATLAB/Simulink model of the motor developed for the 12/8 stator poles to rotor teeth ratio. The modeling makes it possible to improve the design procedure, rationally select the control parameters, and obtain the dynamics of changes in the motor main parameters in various modes of its operation. The dependences of the phase current r.m.s. value and the mean torque on the control parameters, as well as the change in the trolleybus motor rotational speed in the constant power mode are investigated. The proposed switched reluctance motor design version supports all modes of operation according to the trolleybus standard motion diagram in the case of its being equipped with the TAD-3 induction traction motor. In using a reluctance adjustable drive in urban transport vehicles, savings up to 30% of active materials are achieved; only readily available and inexpensive materials are used, and the motor design is well amenable to mass scale production with low labor intensity (the magnetic system has a simple configuration; the motor does not have a commutator, squirrel cage, and permanent magnets, and its coils are machine-wound).

CALCULATION OF TRACTION AND ENERGY CHARACTERISTICS ELECTRIC ROLLING STOCK WITH ASYNCHRONOUS TRACTION ELECTRIC DRIVE

The issue of determining the traction and energy characteristics of electric rolling stock with asynchronous traction drive is considered. It is noted that such rolling stock can work at any point of the traction area, resulting in the need to determine the characteristics of the rolling stock for the entire traction area. The calculation of the characteristics of the traction induction motor, which are the basis for determining the traction and energy characteristics of the electric rolling stock, is considered in detail. A procedure based on the calculation of the replacement circuit of an induction motor is proposed. The calculation of power losses due to higher harmonic voltages and currents is considered. An example of calculation of traction and energy characteristics of an DC electric shunting locomotive with a traction asynchronous electric drive is given.

A Comparison of Prediction Models with Machine Learning Algorithms for Traction Characteristics in Linear Traction Induction Motors

This paper compares the machine learning algorithm‐based prediction methods for the traction characteristics in linear traction induction motors operating at common working conditions, i.e. different slip and running velocity, with a symmetric or asymmetric secondary. These models provide a method for obtaining the dynamic characteristics in the motor that considers nonlinear effects. First, some analytical results for the prototype machine under different working conditions is calculated. Second, classification and feature extraction of traction characteristics results including thrust, transversal and vertical forces is made according to the different slip, running speed and lateral secondary displacement, and the results set is divided into training sets and test sets. Third, the prediction model established by different machine learning algorithms are analyzed and compared in principle. These algorithms in this paper mainly contain: artificial neural networks (ANNs), linear regression (LR), symbolic regression using GP, k‐Nearest Neighbour (kNN), random forests (RFRs). The machine learning algorithm‐based prediction methods are trained with the training set, and then the verified with the test set. Finally, this paper discusses the most optimal model for predicting traction characteristics. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Locomotive asynchronous traction motor rolling bearing fault detection based on current intelligent methods

The paper is devoted to current issues of locomotive asynchronous traction motor (ATEM) fault detection using neural networks. Developed sophisticated intelligent methods for monitoring and inspecting the technical condition of ATE bearings. Current absorption spectra are analysed to assess the technical condition of the induction bearing units. The mechanical vibration frequencies of a squirrel cage induction motor are presented. The method of artificial neural networks which are universal approximators and can effectively and efficiently solve problems of monitoring and diagnostics of technical condition of locomotive induction traction motors is applied. A neural network model and framework for monitoring the technical condition of ATED bearings has been developed. They are based on rules and user-provided facts to recognise the situation, make a diagnosis, formulate a solution or make a recommendation. The main failures of the bearing units of squirrel cage ATED are analysed. A methodology has been developed to build a neural network model of the ATED. The structure and architecture of the artificial neural network is defined. An experimental research has been conducted. The results enable the determination of bearing faults in asynchronous traction motors with squirrel cage rotor.

Single-Observer Based Current Sensor Fault Tolerant Control for IM Traction Drives

In this work, a current sensors fault tolerant control system (FTCS) for induction motor traction drives is proposed. The FTCS uses a single observer for the detection and isolation of disconnection and gain faults occurred in a single or two current sensors available in the drive. Moreover, the observer signals are used for the control reconfiguration within a field-oriented control strategy. The effectiveness of the FTCS (detection, isolation and reconfiguration) and the performance of the control strategy after different faults and perturbations is verified by simulation results using an electric vehicle model.

Inverter Volt-Ampere Capacity Reduction by Optimization of the Traction Synchronous Homopolar Motor

The synchronous homopolar motor (SHM) with an excitation winding on the stator and a toothed rotor is a good alternative to traction induction motors for hybrid mining trucks. The main problem in the design of the SHM electric drives is that the magnetic flux forms three-dimensional loops and, as a result, the lack of high-quality optimization methods, which leads to the need to overrate the installed power of the inverter. This article discusses the procedure and results of optimization of a commercially available 370 kW traction SHM using the Nelder–Mead method. The objective function is composed to mainly improve the following characteristics of the traction SHM: total motor power loss and maximum armature winding current. In addition, terms are introduced into the objective function to make it possible to limit the voltage, the loss in the excitation winding, and the maximum magnetic flux density in the non-laminated sections of the magnetic core. As a result of the optimization, the motor losses and the maximum current required by the motor from the inverter were significantly reduced. The achieved reduction in the maximum current allows the cost of the IGBT modules of the inverter to be reduced by 1.4 times (by $ 2295), and also allows the AC component of the DC-link current to be reduced by the same amount.

A Study of Possible Reasons of the Damage by Electric Current of Traction Induction Motors Bearings of Electric Trains Lastochka and the Ways to Prevent This

There is a problem of damage of bearings of traction induction motors of electric trains Lastochka by electric current. Based on the fact that the bearings have a ceramic insulation coating, and the cases of its electrical breakdown is not fixed, the authors put forward a version that a high-frequency current flows through the bearing as through an electric capacitance. The analysis show that the cause of the current can be a radio standard GSM-R. The proposed technical solution against the damage of bearings: the use of grounding shaft rings, replace the bearings on the other with ceramic rolling elements, arrangement of separate high-frequency grounding for the GSM-R antenna.