The Low-Frequency Oscillation Restrain Method Research of Converter-Fed Multi-Phase Induction Motor Propulsion system

The inverter-fed induction motor drive system may become unstable at low frequencies and light load, and phase current and speed of the induction motor may oscillate periodically, which will threaten safety and reliability of the system. This paper chooses nine-phase induction motor simulated propulsion system as the research object, small disturbance model of three-phase induction motor is built, and average equivalent model of the converter is built by introducing switch function. On the basis above, small disturbance mathematic model of the whole system is obtained. As for the limitation of parameters adjustment method of restrain low-frequency oscillation, the restrain method combining current close-loop with dead-time compensation is put forward. Finally, the proposed restrain method is verified respectively on the built simulation and experimental analogue platform. And the simulation and experimental results indicate that the proposed method can not only satisfy the requirement of low-frequency oscillation restraining, but also be expanded widely, and the stability of the system can get improved greatly.

Role of End-ring Configuration in Shaping IE4 Induction Motor Performance

The performance characteristics,particularly the starting performance of direct line-fed induction motors,which are mainly influenced by the design of the rotor,are crucial considerations for end-users.It is quite a challenging issue for motor manufacturers to enhance the starting performance of existing mass-produced motors with minimal modifications and expenses.In this paper,a simple and cost-effective method to improve the starting performance of a commercial squirrel-cage induction motor(SCIM)is proposed.The influence of geometric parameters of the end-ring on the performance characteristics,including starting(locked rotor)torque,pull-up and break down torque,starting current,rotor electric parameters,current density,power losses,and efficiency have been comprehensively investigated.It has been revealed that among the other end-ring design parameters,the ring thickness has a significant effect on the performance characteristics.An optimal end-ring thickness is determined,and its performance characteristics have been compared to those of its initial counterpart.Numeric and parametric analyses have been conducted using a 2D time-stepping finite element method(FEM).The FEM results were validated using experimental measurements obtained from an 11 kW SCIM prototype.

Harmonics in the Squirrel Cage Induction Motor Analytic Calculation Part III: Influence on the Torque-speed Characteristic

The harmonics that appear in the squirrel cage asynchronous machine have been discussed in great detail in the literature for a long time. However, the systematization of the phenomenon is still pending, so we made an attempt to fill this gap in the previous parts of our study by elaborating formulas for calculation of parasitic torques. It was a general demand among those who work in this field towards the author to verify his formulas with measurements. In the literature, it seems,only one detailed, purposeful series of measurements has been published so far, the purpose of which was to investigate the effect of the number of rotor slots on the torque-speed characteristic curve of the machine. The main goal of this study is to verify the correctness of the formulas by comparing them with the referred series of measurements. Relying on this, the expected synchronous parasitic torques were developed for the frequently used rotor slot numbers-as a design guide for the engineer.Thus, together with our complete table for radial magnetic pull published in our previous work, the designer has all the principles, data and formulas available for the right number of rotor slots for his given machine and for the drive system. This brings this series of papers to an end.

A Stable Fuzzy-Based Computational Model and Control for Inductions Motors

In this paper,a stable and adaptive sliding mode control(SMC)method for induction motors is introduced.Determining the parameters of this system has been one of the existing challenges.To solve this challenge,a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism.According to the dynamic changes of the system,in addition to the parameters of the SMC,the parameters of the type-2 fuzzy neural network are also updated online.The conditions for guaranteeing the convergence and stability of the control system are provided.In the simulation part,in order to test the proposed method,several uncertain models and load torque have been applied.Also,the results have been compared to the SMC based on the type-1 fuzzy system,the traditional SMC,and the PI controller.The average RMSE in different scenarios,for type-2 fuzzy SMC,is 0.0311,for type-1 fuzzy SMC is 0.0497,for traditional SMC is 0.0778,and finally for PI controller is 0.0997.

Performance and Safety Improvement of Induction Motors based on Testing and Evaluation Standards

The induction motor,which converts electrical energy into mechanical energy,has been recognized as the cornerstone of industrialization.The rotor of an induction motor can be either a squirrel cage rotor or a wound-type rotor,both existing as magnetless topologies.Three-phase squirrel cage induction motors are frequently utilized in industrial drives because they are dependable,have high starting torque,are selfstarting and affordable.Single-phase induction motors,on the other hand,are commonly used for small loads such as domestic appliances in form of modest fans,pumps and electric power tools.In South Africa,there have been reports of fires and explosions resulting in live and property loss because of induction motors that have not been thoroughly tested or are incorrectly labelled in terms of ratings,electrical safety and performance.The goal of this study is targeted at preventing end-user injuries and failures caused by non-compliant induction motors,by evaluating locally manufactured/imported induction motors based on tests and evaluation from standards(IEC and SANS).The study is conducted using experimental procedures at the Explosion Prevention Technology and Rotating Machines(EPT and RM)laboratory,South African Bureau of Standards(SABS),South Africa.The main finding from the study shows differences in the nameplate characteristics of various induction motors which could have detrimental effects such as production and operational downtime in their end-use industries,at later stages.

Enhanced Fuzzy Logic Control Model and Sliding Mode Based on Field Oriented Control of Induction Motor

In the context of induction motor control, there are various control strategies used to separately control torque and flux. One common approach is known as Field-Oriented Control (FOC). This technique involves transforming the three-phase currents and voltages into a rotating reference frame, commonly referred to as the “dq” frame. In this frame, the torque/speed and flux components are decoupled, allowing for independent control, by doing so, the motor’s speed can be regulated accurately and maintain a constant flux which is crucial to ensure optimal motor performance and efficiency. The research focused on studying and simulating a field-oriented control system using fuzzy control techniques for an induction motor. The aim was to address the issue of parameter variations, particularly the change in rotor resistance during motor operation, which causes the control system to deviate from the desired direction. This deviation implies to an increase in the magnetic flux value, specifically the flux component on the q-axis. By employing fuzzy logic techniques to regulate flux vector’s components in the dq frame, this problem was successfully resolved, ensuring that the magnetic flux value remains within the nominal limits. To enhance the control system’s performance, response speed, and efficiency of the motor, sliding mode controllers were implemented to regulate the current in the inner loop. The simulation results demonstrated the proficiency of the proposed methodology.

LOW COST IMPLEMENTATION OF SPEED SENSORLESS INDUCTION MOTOR CONTROL USING STATOR FLUX ORIENTATION

The control platform of the induction motor （IM） with low costs is developed by using DSP MC56F8013 with a good performance/price rtaio. The control algorithm for the speed sensorless IM is studied based on the stator flux orientation （SFO）. The algorithm structure is simple to be implemented and cannot be influenced by motor parameters, The improved stator flux estimation is used to compensate errors caused by the low pass filter （LPF）. A new speed regulator is designed to ensure the system working with the maximal torque in the transient state. The system simulation and the prototype experiment are made. Results show that the con- trol system has good dynamic and static performance.

INDUCTION MOTOR SPEED CONTROL SYSTEM BASED ON FUZZY NEURAL NETWORK

A fuzzy neural network controller with the teaching controller guidance and parameter regulations for vector-controlled induction motor is proposed. The design procedures of the fuzzy neural controller and the teaching controller are described. The parameters of the membership function are regulated by an on-line learning algorithm. The speed responses of the system under the condition, where the target functions are chosen as I qs and ω, are analyzed. The system responses with the variant of parameter moment of inertial J, viscous coefficients B and torque constant K tare also analyzed. Simulation results show that the control scheme and the controller have the advantages of rapid speed response and good robustness.

Speed Sensorless Vector Control of Induction Motor Based on Reduced Order Extended Kalman Filter

A speed sensorless vector control system of induction motor with estimated rotor speed and rotor flux using a new reduced order extended Kalman filter is proposed. With this method, two rotor flux components are selected as the state variables, and the rotor speed as an estimated parameter is regarded as an augmented state variable. The algorithm with reduced order decreases the computational complexity and makes the proposed estimator feasible to be implemented in real time. The simulation results show high accuracy of the estimation algorithm and good performance of speed control, and verify the usefulness of the proposed algorithm.

Design and Development of the DTC Induction Motor Drive for Electric Vehicle

The design and development of the traction controller for electric vehicle is introduced, which is based on the induction motor. This drive is developed by using a digital signal processor at low cost and carried out with the module design concept of both software and hardware. Nevertheless, a scheme of the sensorless direct torque control is based on the developed hardware, of which the feasibility is tested by a trial program. Additionally, both the interface function of the drive hardware and the feasibility of its software are proved to be good by the trail programs. A test motor can run about 18?r/min by a variable frequency program with the space vector pulse width modulation technology, of which the torque is visible pulsatile. In this presentation, based on the theoretical approach, the sensorless torque control is to be studied and applied to electric vehicles, of which the quick, smooth and stable torque response is emphasized because it quite benefits improving the drive performance of electric vehicles.

An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

Induction motors （IMs） are commonly used in various industrial applications. To improve energy con- sumption efficiency, a reliable IM health condition moni- toring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is pro- posed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are syn- thesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air- gap eccentricity diagnosis. The effectiveness of the pro- posed harmonic synthesis technique is examined experi- mentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.

Nonlinear Decoupling Control of the Bearingless Induction Motors Based on the Airgap Motor Flux Orientation

The bearingless induction motor, which combines the inductionmotor and magnetic bearing is a strongly coupled complicatednonlinear system; the decoupling control of the electromag- net toqueand readial levitation force is the base of the stable operation ofthe benaringless motor. In this paper, the air-gap motor fluxoriented vector control is proposed to realize the decoupling controlof this nonlinear system even in the transient case based on thelevitation principle. Simulations show the stable suspension and goodperformance of the proposed algorithm.

A novel induction motor control scheme using IDA-PBC

A new control scheme for induction motors is proposed in the present paper, applying the interconnection and damping assignment-passivity based control （IDA-PBC） method. The scheme is based exclusively on passivity based control, without restricting the input frequency as it is done in field oriented control （FOC）. A port-controlled Hamiltonian （PCH） model of the induction motor is deduced to make the interconnection and damping of energy explicit on the scheme. The proposed controller is validated under computational simulations and experimental tests using an inverter prototype.

Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estima- tion cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the fre- quencies of the fundamental and fault characteristic com- ponents with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.

Fourier and wavelet transformations application to fault detection of induction motor with stator current

Fault detection of an induction motor was carried out using the information of the stator current. After synchronizing the actual data, Fourier and wavelet transformations were adopted in order to obtain the sideband or detail value characteristics under healthy and various faulty operating conditions. The most reliable phase current among the three phase currents was selected using an approach that employs the fuzzy entropy measure. Data were trained with a neural network system, and the fault detection algorithm was verified using the unknown data. Results of the proposed approach based on Fourier and wavelet transformations indicate that the faults can be properly classified into six categories. The training error is 5.3×10-7, and the average test error is 0.103.

Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor

This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.

Sensorless torque control scheme of induction motor for hybrid electric vehicle

In this paper, the sensorless torque robust tracking problem of the induction motor for hybrid electric vehicle （HEV） applications is addressed, Because motor parameter variations in HEV applications are larger than in industrial drive system, the conventional field-oriented control （FOC） provides poor performance. Therefore, a new robust PI-based extension of the FOC controller and a speed-flux observer based on sliding mode and Lyapunov theory are developed in order to improve the overall performance. Simulation results show that the proposed sensorless torque control scheme is robust with respect to motor parameter variations and loading disturbances. In addition, the operating flux of the motor is chosen optimally to minimize the consumption of electric energy, which results in a significant reduction in energy losses shown by simulations.

A Loss-model-based Efficiency Optimization Control Method for Induction Traction System of High-speed Train under Emergency Self-propelled Mode

Increasing attention has been paid to the efficiency improvement of the induction traction system of high-speed trains due to the high demand for energy saving. In emergency self-propelled mode, however, the dc-link voltage and the traction power of the motor are significantly reduced, resulting in decreased traction efficiency due to the low load and low speed operations. Aiming to tackle this problem, a novel efficiency improved control method is introduced to the emergency mode of high-speed train traction system in this paper. In the proposed method, a total loss model of induction motor considering the behaviors of both iron and copper loss is established. An improved iterative algorithm with decreased computational burden is then introduced, resulting in a fast solving of the optimal flux reference for loss minimization at each control period. In addition, considering the parameter variation problem due to the low load and low speed operations, a parameter estimation method is integrated to improve the controller's robustness. The effectiveness of the proposed method on efficiency improvement at low voltage and low load conditions is demonstrated by simulated and experimental results.

Linearizing Control of Induction Motor Based on Networked Control Systems

A new approach to speed control of induction motors is developed by introducing networked control systems （NCSs） into the induction motor driving system. The control strategy is to stabilize and track the rotor speed of the induction motor when the network time delay occurs in the transport medium of network data. First, a feedback linearization method is used to achieve input-output linearization and decoupling control of the induction motor driving system based on rotor flux model, and then the characteristic of network data is analyzed in terms of the inherent network time delay. A networked control model of an induction motor is established. The sufficient condition of asymptotic stability for the networked induction motor driving system is given, and the state feedback controller is obtained by solving the linear matrix inequalities （LMIs）. Simulation results verify the efficiency of the proposed scheme.

Broken Rotor Bar Fault Detection of Induction Motors Using a Joint Algorithm of Trust Region and Modified Bare-bones Particle Swarm Optimization

A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence e ect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm(called TR-MBPSO) based on a modified bare-bones particle swarm optimization(BPSO) and trust region(TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10^(-4), but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.