Speed Of Induction Motor Research Articles

SENSORLESS SPEED ESTIMATION OF THREE PHASE INDUCTION MOTORS BASED ON DYNAMIC MODEL

In speed control systems of induction motor electrical drives, real-time speed monitoring is necessary. Speed monitoring can be done using the direct method, which uses a mechanical sensor mounted on the motor shaft, or the indirect method, which is based on estimation, mainly from the dynamic model of the motor. Speed estimation based on the dynamic model of the motor in orthogonal coordinates is the most widespread method in sensorless speed control systems of three-phase squirrel cage induction motor electrical drives, especially those of high accuracy. This paper presents the open-loop speed estimator used for speed estimation in three-phase induction motors. The proposed speed estimators are based on the orthogonal coordinate’s dynamic model of an induction motor in a stator reference frame. This technical solution is simple and has a low cost. The currents and voltages of the two motor phases are the input variables of the estimator, while the output variable is the induction motor speed. The speed estimator model is built using LabVIEW software. The dynamics and accuracy of the estimator proposed in this paper have been tested experimentally. The speed measured by the industrial incremental encoder is compared with that of a speed estimator modeled in LabVIEW software. The obtained experimental results show a good match between the measured and estimated speeds under the step torque load changes of the induction motor.

New disturbance observer-based speed estimator for induction motor

<p>This paper discusses a novel disturbance observer designed as an estimator to determine the rotor speed of an induction motor. This observer is a solution to obtain a simple structure with a small number of compact observer gains. Furthermore, the adaptation law is no longer required to estimate induction motor speed values. This is a machine model-based computation method that uses a stationary reference frame. The nonlinearity problem is solved using an additional state vector in the observer model, which is known as an extended state observer. This approach easily and systematically determines the observer gain by applying the linear quadratic regulator (LQR) method, thereby avoiding time-consuming trial errors. The proposed observer, which was presented in both continuous and discrete forms, was tested using a sensorless V/f- controlled induction motor. The simulation results show that the proposed observer can accurately estimate all states, namely, the rotor flux and stator current; therefore, the proposed estimator provides the speed and electromagnetic torque for a wide operational range of speeds and load torques. It was also shown that the proposed observer was robust to noise and uncertainty in induction motor parameters.</p>

Fractional-based iterative learning-optimal model predictive control of speed induction motor regulation for electric vehicles application

Introduction. A new control strategy based on the combination of optimal model predictive control (OMPC) with fractional iterative learning control (F-ILC) for speed regulation of an induction motor (IM) for electric vehicles (EVs) application is presented. OMPC uses predictive models to optimize speed control actions by considering the dynamic behavior of the IM, when integrated with the F-ILC, the system learns and refines the speed control iteratively based on previous iterations, adapting to the specific characteristics of the IM and improving performance over time. The synergy between OMPC and F-ILC named F-ILC-OMPC enhances the precision and adaptability of speed control for IMs in EVs application, and optimizes the energy efficiency and responsiveness under varying driving conditions. The novelty lies in the conjunction of the OMPC with the ILC-based on the fractional calculus to regulate the speed of IMs, which is original. Purpose. The new control strategy provides increased performance, robustness and adaptability to changing operational conditions. Methods. The mathematical development of a control law that mitigates the disturbance and achieves accurate and efficient speed regulation. The effectiveness of the suggested control strategy was assessed via simulations in MATLAB conducted on an IM system. Results. The results clearly show the benefits of the F-ILC-OMPC methodology in attaining accurate speed control, minimizing steady-state error and enhanced disturbance rejection. Practical value. The main perspective lies in the development of a speed control strategy for IMs for EVs and the establishment of reliable and efficient electrical systems using ILC-OMPC control. This research has the prospect of a subsequent implementation of these results in experimental prototypes. References 24, tables 2, figures 9.

Lyapunov-based neural network model predictive control using metaheuristic optimization approach

This research introduces a new technique to control constrained nonlinear systems, named Lyapunov-based neural network model predictive control using a metaheuristic optimization approach. This controller utilizes a feedforward neural network model as a prediction model and employs the driving training based optimization algorithm to resolve the related constrained optimization problem. The proposed controller relies on the simplicity and accuracy of the feedforward neural network model and the convergence speed of the driving training based optimization algorithm. The closed-loop stability of the developed controller is ensured by including the Lyapunov function as a constraint in the cost function. The efficiency of the suggested controller is illustrated by controlling the angular speed of three-phase squirrel cage induction motor. The reached results are contrasted to those of other methods, specifically the fuzzy logic controller optimized by teaching learning-based optimization algorithm, the optimized PID with particle swarm optimization algorithm, the neural network model predictive controller based on particle swarm optimization algorithm, and the neural network model predictive controller using driving training based optimization algorithm. This comparative study showcase that the suggested controller provides good accuracy, quickness and robustness due to the obtained values of the mean absolute error, mean square error root mean square error, enhancement percentage, and computing time in the different simulation cases, and it can be efficiently utilized to control constrained nonlinear systems with fast dynamics.

Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle

Abstract Electric vehicles (EVs) cut greenhouse gas emissions and our use of non-renewable resources, making them more attractive. EVs have lower fuel and maintenance expenses than internal combustion engine automobiles. This study proposes a multi-converter/Multi‒Machine system with two induction motors (IM) that drive a pure EV’s rear wheels. EV two-stage controllers using a simple Adaline neural network (NN) regulate Field-Oriented regulate of a three-phase IM. To control IM speed, the first controller level is a hybrid proportional–integral (PI) with a robust integral sign of error (RISE) controller. Injection torque is controlled by PI‒adaline NN in the second controller step. The simple Adaline NN improves two-stage controller performance. The Multi-Verse Optimization algorithm found the ideal RISE parameter to improve EV drive system performance. A plug-in EV’s linear speed is controlled by the Electronic Differential Controller (EDC). It uses the driver’s reference speed and steering angle to set each driving wheel’s reference speed. EDC adjusts wheel speeds to enhance traction and stability during cornering, accelerating, and decelerating. Utilizing this information, the EDC can effectively distribute power and torque to the wheels, thereby enhancing vehicle handling and overall performance. Three distinct road scenarios and the designated driving route topology have been used to act and demonstrate the resistive forces that affected the EV while it was traveling down the road. By using Matlab (Simulink), EV’s roadworthiness and efficiency will be evaluated.

The impact of Adaptive Fuzzy Logic on the regulation of induction motor speed

The impact of Adaptive Fuzzy Logic on the regulation of induction motor speed

Derating factor determination of the three‐phase induction motor under unbalanced voltage using pumping system

AbstractA novel method is presented for determining the derating factors of a three‐phase induction motor under the condition of the unbalanced supply voltages. In this method, a mechanical system is used which consist of the a centrifugal pump, two valves, a DC motor, which are connected to the shaft of the three‐phase induction motor. A sliding mode control system is used for position control of the DC motor for adjusting the valve angle for derating the induction motor. The authors present the results of an experiment in which a three‐phase induction motor was subjected to various unbalanced voltage conditions. The results of simulations were used to look into what happened when there were different levels of imbalanced voltage. This was done to determine how these situations changed an induction motor's speed, torque, and efficiency. For this system, the stator current would be greater than the rated current if there was an imbalance in the supply voltage. Therefore, to reduce the amount of power that the three‐phase induction motor can produce, the control system uses a DC motor to reduce the angle of one of the two valves. This decreasing angle continues until the root mean square value of the stator current returns to the rated current. At this point, the derating factor may be calculated by dividing the output power of the three‐phase induction motor in the unbalanced condition by the output power when there are ideal sinusoidal. The MATLAB SIMULINK environment is utilised to perform simulations of the proposed system.

Design and modeling of a proportional derivative controller for a three-phase induction motor speed

Design and modeling of a proportional derivative controller for a three-phase induction motor speed

A non-intrusive technique for estimating the efficiency of low voltage three-phase induction motors

This paper introduces a non-intrusive technique based on the air–gap torque (AGT) method to estimate the efficiency of induction motors (IMs). The technique uses a 3rd-degree equation to calculate the sum of windage and friction losses (FWL) and rotor stray load losses (SLLr), considering the number of IM poles. IM speed is estimated using motor current signature analysis, and a new procedure to estimate stator resistance is presented. Efficiency is then determined by applying the estimated rotor speed, stator resistance, and the sum of FWL and SLLr in the AGT equations. This method requires only nameplate information, IM line voltages, and currents. Simulations and experimental tests for balanced, unbalanced, and harmonic feeding modes show the proposed technique’s estimated efficiency values are very close to measured values, unlike the AGT method. This demonstrates the proposed technique’s high accuracy, making it suitable for non-intrusive efficiency estimation of in-service IMs in industrial programs.

Sliding-Mode Control of Linear Induction Motor Based on Exponential Reaching Law

As a strongly coupled, multivariable, high-order nonlinear, time-varying complex system, a linear induction motor is susceptible to outside disturbances and mismatched parameters. Because of its straightforward control mechanism and fixed PI value, the traditional PI regulator is frequently utilized in linear induction motor speed control systems. However, because of these limitations, it frequently falls short of meeting the high-performance control needs of the motor in complex operating conditions. In order to solve the contradiction between the sliding-mode motion reaching time and vibration, the sliding-mode variable structure control is applied to the linear induction motor speed control system in this paper. The sliding-mode controller is designed using the exponential reaching law method, and the designed system is analyzed through simulation. The findings demonstrate how the controller enhances the system’s robustness and disturbance resistance by bringing about the advantages of rapidity, stability, no overshooting, and a strong resistance to load disturbance.

A non-isolated PFC bridgeless SEPIC-Cuk converter with adaptive PI controller for induction motor

In general, the induction motor (IM) is extremely nonlinear in nature and frequency dependent. In most cases, the power generated by the IM has a low power factor (PF), which exhibits detrimental effect on the extent to which the whole transmission and distribution system functions. Since there exists more current harmonics as an outcome of minimized PF, the efficiency of the power system suffers due to transmission line heating and voltage distortion characteristics. Therefore, this paper proposes a power factor correction (PFC) method to overcome the aforementioned issues. Here, by the utilization of AC-DC bridgeless SEPIC-Cuk converter, the power quality is improved by reducing reactive power consumption and enabling better control of voltage and current outputs. To maintain the stable DC link voltage with reduced ripples, the adaptive proportional-integral (PI) controller is used in this work. The three-phase voltage source inverter (VSI) transitioning function is controlled by cascaded fuzzy logic (CFL) controller, which is also utilized for regulating the speed of the three-phase IM. Implementing the proposed control strategy improves power quality significantly by reducing total harmonic distortion (THD). The proposed system is simulated in the MATLAB platform and the attained outcomes, it is clear that the proposed system is highly effective.

Sensorless speed controller of an induction motor with MRAS-based model predictive control

Speed sensors play critical role in induction motor (IM) control systems. However, the use of these sensors cause several drawbacks such as increasing the overall cost, decreased reliability on control and increased noise problems in data acquisition. The sensorless control methods also have been proposed in control systems of IM to increase the overall performance. On the other hand, model predictive controllers (MPCs) which is one of the most recently used controller methods require the speed measurement to perform speed and torque control of an IM. In addition to control method, the topology of voltage source inverter (VSI) affects the overall efficiency of the IM drive because of its losses and total harmonic distortion (THD) ratios delivered by the topology. The main proposal of this study is to implement an IM drive built with a three-level active neutral point clamped (3L-ANPC) voltage source inverter (VSI) and to propose an MPC that eliminates sensor requirement in speed control of IM. The proposed IM drive takes benefits of multilevel inverter topology, which reduces the torque ripple, enables low switching frequency and provides decreased harmonic distortion. The switching pulses of ANPC VSI are generated by the proposed sensorless MPC method for controlling the torque of IM. The modelling and experimental verification of the ANPC based IM drive have been carried out and the sensorless MPC controller has been validated in this study. There two types of model reference adaptive system (MRAS) have been proposed to ensure precise estimation of IM speed that should be supplied to MPC controller. These MRAS methods are titled as the rotor flux based model reference adaptive system (MRASF), and the stator current based model reference adaptive system (MRASCC) according to estimation references. The proposed MPC-based IM drive has been analyzed under variable speed and torque circumstances to detect the efficiency and reliability of MRASF and MRASCC algorithms. The efficiency indicators such as flux and torque responses, and speed control of the suggested MPC IM drive are validated with experimental analysis. The results of revealed that the IM effectively tracks the reference speed and produces the required torque depending to the precise control of proposed MPC algorithm.

An Implementation of Fuzzy to Minimize Electrical Losses in Induction Machine Vector Control

An induction motor is a motor that is difficult to control so it requires synchronous speed due to dynamic load changes which are controlled by the torque current while the field current is kept constant. Thus, in this paper, we implement an adaptive PID controller method with an induction motor using MATLAB which coordinates the speed of two decoupled loads with a three-phase induction motor, where an adaptive Self Tuning Regulator (STR) PID controller is designed and realized to regulate the speed of a three-phase induction motor. using MATLAB software and AVR Atmega16 microcontroller as input and output from the plant with communication using serial RS232. The results show that after coordinating the system with each motor which is given a neural controller and FOC, the motor speed results are obtained which gradually provide synchronous speed so it can be concluded that this system can work well to slow down the plant response. Keywords: Induction Motor, Motor Speed, neural method, PID controller

Speed Control of Induction Motors using Neuro Fuzzy Expert Systems

Induction motors and their speed control is critical aspect in several real life applications. Often, the proportional integral derivative (PID) controlling mechanism is found in several applications in industries where speed of motors or induction motors is to be controlled. The PID controller is specifically useful since it tries to minimize the steady state error as well as increase the response or speed of the system, thereby incorporating the benefits of proportional derivative and proportional integral control. However, the real time operation of PID controllers is challenging due to its tuning. The controlling mechanism is critically important for the application of the PID. Previously, manual tuning was used which needed experts and was also prone to errors. With the advent of sophisticated optimization tools, automatic tuning has gained momentum. In this work, a combination of neural networks and fuzzy logic often called neuro fuzzy expert systems has been proposed for the speed control of induction motors. It has been shown that proposed system is capable to attain better results compared to conventional techniques. The system has been designed on Matlab/Simulink. Key Words: Induction Motors, Speed Control, PID Controllers, Fuzzy Logic, Neuro Fuzzy Expert Systems.

Controlling of Induction Motor

Abstract: This paper describes how to use an Arduino Uno to regulate the speed of an induction motor and create an energyefficient, smooth control system. Due to their affordability and adaptability, induction motors are frequently utilized in industrial drives; nevertheless, the rangeof speed control available with stator control is limited. Controlling the firing angle and utilizing power electrical gadgets can help fix this issue. The goal is to use stator control to regulate the induction motor's speed. This hardware provides a signal to the microprocessor, which manages the power supply to the electronic devices. Through the use of an Arduino Uno to adjust the firing angle of a semiconductor power device, the motor's stator winding's terminal voltage canbe controlled

Intelligent fuzzy back-stepping observer design based induction motor robust nonlinear sensorless control

Introduction. The control algorithm of Induction Motor (IM) is massively dependent on its parameters; so, any variation in these parameters (especially in rotor resistance) gives unavoidably error propagates. To avoid this problem, researches give more than solution, they have proposed Variable Structure Control (VSC), adaptive observers such as Model Reference Adaptive System, Extended Luenberger Observer (ELO) and the Extended Kalman Filter (EKF), these solutions reduce the estimated errors in flux and speed. As novelty in this paper, the model speed observer uses the estimated currents and voltages as state variables; we develop this one by an error feedback corrector. The Indirect Rotor Field Oriented Control (IRFOC) uses the correct observed value of speed; in our research, we improve the observer’s labour by using back-stepping Sliding Mode (SM) control. Purpose. To generate the pulse-width modulation inverter pulses which reduce the error due of parameters variations in very fast way. Methods. We develop for reach this goal an exploration of two different linear observers used for a high performance VSC IM drive that is robust against speed and load torque variations. Firstly, we present a three levels inverter chosen to supply the IM; we present its modelling and method of control, ending by an experiment platform to show its output signal. A block diagram of IRFOC was presented; we analyse with mathematic equations the deferent stages of modelling, showed clearly the decoupling theory and the sensorless technique of control. The study described two kinds of observers, ELO and EKF, to estimate IM speed and torque. By the next of that, we optimize the step response using the fuzzy logic, which helps the system to generate the PI controller gains. Both of the two observers are forward by SM current controller, the convergence of SM-ELO and SM-EKF structures is guaranteed by minimizing the error between actual and observed currents to zero. Results. Several results are given to show the effectiveness of proposed schemes.

Implementation of Induction Motor Speed Control Using a PID Controller

The three-phase induction machine that is used has a constant speed in accordance with the factory capacity without being able to adjust the speed. The inverter is a motor speed control device by changing the frequency value that enters the motor. Setting the frequency value is intended to obtain the desired rotation speed or as needed. But the inverter itself needs to be added with a controller so that the control value of the motor can be adjusted according to the desired needs. PID controller is a controller that is most widely used in the field of control systems in the industrial world. Combining the three PID constants in parallel can cover every disadvantage and strength of each constant so that a good controller is obtained. Based on the results of the experiments that have been carried out, it can be concluded that the speed of the three-phase induction motor can work well. When tested by giving values of Kp = 02, Ki = 0 and Kd = 3.7, it was found that the motor speed response had a Time Constant = 1.5 seconds, Rise time = 2.2 seconds and Settling time = 2.7 seconds.

Rancang Bangun Multilevel Inverter dengan Filter Pasif L-C-L dan Teknologi IoT Untuk Memantau Perubahan Kecepatan Motor Induksi Satu Fasa

Multilevel inverter is a type of inverter used to generate AC signals with adjustable frequency and better quality. L-C-L passive filter is used to eliminate harmonic distortion in the generated AC signals. Meanwhile, IoT technology is used to remotely monitor and control the speed of the motor through a mobile application. In this thesis, the design of a flying capacitor multilevel inverter is used as an induction motor speed controller with the resulting Total Harmonic Distortion (THD) can be reduced if you add a level to the inverter. Then, L-C-L passive filter is designed to eliminate harmonic distortion in the generated AC signals. Furthermore, the implementation of IoT technology is carried out using Blynk IoT platform. The test results show that the multilevel inverter with L-C-L passive filter successfully generates AC signals with better quality compare without L-C-L passife filter. In addition, IoT technology is successfully implemented and allows users to remotely monitor and control the speed of the motor through a mobile application. Thus, the use of multilevel inverter with L-C-L passive filter and IoT technology can effectively increase the efficiency and control of single-phase induction motor.
 Keywords: multilevel inverter, L-C-L passive filter, IoT, induction motor, total harmonic distortion.

A Noval Method of Single Phase to Three Phase Cyclo Converter for Three Phase Induction Motor Drive

This paper present the study on analysis of speed of induction motor when the motor is made to run on different loads torque. fed by single phase to three phase cyclo converter.. Normally, constant voltage constant frequency single phase AC or three phase AC is used. However in some rural areas, only a single phase utility is available, so it became necessary to convert single phase supply to three phase supply to run three phase induction motors. For this purpose, cyclo converter model is patterned by MATLAB/SIMULINK SOFTWARE to convert single phase supply to three phase supply. Converter model employs only six IGBT so the resulting Cyclo converter-motor drive system is cheap and compact, As we know that the rating of the machine increases with the increase in number of phase .eg. output of three phase motor is 1.5 times the output of a single phase motor of same size. therefore it is beneficial to use three phase induction motor instead of single phase induction motor for low industrial applications

Study and Evaluation a New Predictive Control Method for Speed and Stator Current Control of Induction Motor

It is clear that modern industries rely heavily on electric motors, especially induction motors. These motors convert electrical energy into mechanical energy. The distinction in the performance of the induction motor lies in that it is powerful when exposed to various operational and environmental variables, and it is also inexpensive. However, there are many traditional disadvantages that appear during the operation of the induction motor in its non-linear mechanical properties in addition to the difficulty in regulating the speed of the motor. In this paper , we present a new control method for controlling the stator current and speed of induction motor based on current control with speed control technique. The present model is based on conventional predictive controller development with a structure which is similar to rotor control and the direct torque control .It has double loops and both loops will use the prediction power. The inner loop controls the stator current based on Finite Control Set - Model Predictive Control (FCS-MPC) and the outer loop controls the speed to maximize the dynamic response of the loop. The MATLAB software has been used to implement the controller circuit. The obtained simulation results indicates that the presented control method has comparable performance to conventional controllers with some reduction in the overshoot and fast response interval.