Indirect Vector Controlled Induction Motor Research Articles

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

Artificial intelligence and PI gain optimisation for sensorless indirect vector control of induction motor-based electric vehicle drives

This paper aims to develop indirect vector-controlled induction motor (IM) drives for EV applications. In this paper, controllers are used in two places. The first is the speed controller, used to reduce the error between reference speed and actual EV speed. And the second one is the speed estimator used to reduce the error between the adaptive model and reference model during sensorless operation. The performance of these controllers is compared by using ANFIS, ANN and PI controllers. The real-time performance of the IM-based EV drive system is dependent on the PI gain value. Hence in this paper, seven human and nature-inspired metaheuristic optimisation techniques such as JFO, GWO, ABC, PSO, GA, TLBO and DE are used to obtain the optimised value of the PI controller.

PERFORMANCE ANALYSIS AND SPEED CONTROL USING INDIRECT VECTOR CONTROLLED FOR INDUCTION MOTOR DRIVES

This paper presents the speed control scheme of an indirect vector-controlled induction motor (IM) drive. PWM controlling scheme is based on Voltage source inverter type space vector pulse width modulation (SVPWM) and the Conventional-PID controller or Fuzzy-PID controller is employed in closed-loop speed control. Decoupling of the stator current into torque and flux producing (d-q) current components model of an induction motor is involved in the indirect vector control. The torque component Iq current of an IM is developed by an intelligent-based Fuzzy PID controller. Based on settling time and dynamic response the performance of the Fuzzy Logic Controller is compared with that of the PID Controller to sudden load changes. It provides better control of motor torque with high dynamic performance. The simulated design is tested using various toolboxes in MATLAB. Simulation results of both controllers are presented for comparison.

Design and Modeling of Hybrid Electric Vehicle Powered by Solar and Fuel Cell Energy with Quadratic Buck/Boost Converter

Considering technological advancements and international standards mandating reduced greenhouse gas emissions, automobile manufacturers have shifted their focus toward innovative technologies related to fuel-cell electric vehicles. Despite having an all-electric powertrain, fuel cell electric vehicles (FCEVs) utilize a fuel cell stack as their energy source, which runs on hydrogen and results in the emission of only water and heat. Due to the absence of tailpipe pollutants, fuel-cell electric vehicles are considered zero-emission vehicles. Among fuel cell types, low-temperature and low-pressure fuel cells, such as Proton Exchange Membrane Fuel Cells (PEMFCs), are well-suited for vehicular applications as they exhibit high power density, operate at lower temperatures (60-80°C), and are less prone to corrosion than other types of fuel cells. The main objective of this paper is to investigate solar-assisted electric fuel cell vehicles that efficiently integrate the fuel cell system with an electrolyzer and solar power to fulfill the fluctuating power demands of the electric motor and auxiliary systems. A novel EV configuration with a fuel cell, electrolyzer and onboard PV cell is proposed. An onboard PV cell can assist the fuel cell when the irradiation is enough to generate the power. During the idle conditions of vehicles, PV-generated power can be converted into chemical energy using an electrolyzer and generated hydrogen can be stored in a hydrogen tank. To match the voltage required by the motor and sources a quadratic bidirectional buck-boost converter is employed. The proposed configuration is examined by considering variable irradiance and variable speed values. To obtain the maximum power output from the photovoltaic (PV) panel, a Maximum Power Point Tracking (MPPT) algorithm is employed to regulate the PV system. To enhance the efficiency and cost-effectiveness of PV systems, an enhanced version of the incremental conductance algorithm is utilized as the MPPT control strategy. Outer voltage and inner current control are adopted to regulate the DC output voltage of the QBBC converter. An indirect vector-controlled induction motor is used as vehicle drive. Simulations are performed to investigate the proposed EV configuration in MATLAB/SIMULINK.

Smoothed Kalman MRAS observer for speed sensorless indirect vector controlled induction motor drives

This paper proposes a modified Kalman filter algorithm based on fixed lag smoothing which can be used to estimate the rotor speed and flux of three phase induction motor. The behaviour of a typical extended Kalman filter (EKF) algorithm is influenced by the process and measurement error covariance matrices. In EKF, these matrices are chosen by trial and error method and have to be varied according to the varying operating conditions of the motor. In this paper a smoothing-based extended Kalman filter (SKF) algorithm which uses additional data points for estimation is proposed. Since additional data is used, the algorithm is able to give a better estimate, with the same values of process and measurement error covariance matrices. The performance of the proposed algorithm is explored for sensorless indirect vector control application of three-phase induction motor. Finally, in order to validate the superiority of this algorithm, it is compared with the performance of EKF algorithm for various reference speeds in real-time.

Smoothed Kalman MRAS observer for speed sensorless indirect vector controlled induction motor drives

Smoothed Kalman MRAS observer for speed sensorless indirect vector controlled induction motor drives

Enhanced Performance of Indirect Vector Controlled Induction Motor Drive with a Modified Type 2 Neuro-Fuzzy Torque Controller in Interfacing with dSPACE DS-2812

Due to the nonlinearities of the PI controller, the performance of the PI controller is not satisfactory. The gains must be properly selected after changes in control parameters is one of the issues of the PI controller. The modified type 2 Neuro-Fuzzy torque controller of indirect vector control-based induction motor drive is proposed in this paper by taking single input as an error i.e. speed and torque against two inputs error and change in error of conventional T2NFC.The superiority of fuzzy and neural networks has been utilized by T2NFC as type 2 MF’s consist of fuzzy and FOU. The intersection point of the membership function is smaller so that the value of the centroid method is more precise than the T1NFC. The induction motor parameters, such as stator phase current, speed, and torque of the proposed T2NFC are simulated in MATLAB at different operating conditions and compared with PI, T1NFC controllers. The proposed T2NFC significantly minimizes the ripples in torque of the induction motor in comparison with PI and T1NF controllers. The practical implementation is also carried out with a 3.7 KW induction motor using DSP 2812 controller to analyse induction motor parameters in real-time.

Search controller‐based online efficiency optimisation strategy for induction motor drives using modified adaptive quadratic interpolation

A search control-based new efficiency optimisation strategy for indirect vector controlled induction motor (IM) drive is proposed in this work. The online optimisation method considered in this regard is based on modified adaptive quadratic interpolation. The proposed strategy is employed in the flux loop of the speed control technique to decide the optimum flux component of stator current necessary to meet a particular drive operating condition. The selection of the optimum flux in this way, in fact, minimises the core loss of IM drive, thereby improving the overall efficiency of the drive depending upon the motor loading condition. Like the conventional search controllers, the algorithm is simple, immune to the machine parameter variation and does not require any additional circuitry for its implementation. The algorithm offers faster convergence, thereby preserving the dynamic response of the speed control loop. Analytical studies are done in MATLAB/Simulink and the results are validated experimentally in a 2.2 kW IM drive laboratory prototype.

A Comparative Performance Analysis of Indirect Vector Controlled Induction Motor Drive Using Optimized AI Techniques

This paper exhibits a point by point comparison between Neuro Fuzzy and Genetic Algorithm GA based control systems of Induction Motor drive, underlining favorable circumstances and drawbacks. Industries are advancing and upgrading generation line to enhance efficiency and quality. Induction machines are considered by nonlinear, time varying dynamics, inaccessibility of few states and thus can be considered as a challenging issue. In this paper, a novel method using modified GA is presented to limit electric losses of Induction Motor and it is compared with Neuro Fuzzy Controller. GA is a subordinate of AI, whose principle relies upon Darwin’s theory—struggle for existence and the survival of the fittest. The technique for deciding the gain parameters of PI controller utilizing GA whose output is utilized to control the torque applied to the Induction Motor in this way controlling its speed. The gains of PI controller are improved with the assistance of GA to upgrade the performance of IM drive. The results are simulated in MATLAB Simulink and are related with the conventional PI controller and Adaptive Neuro Fuzzy controller (NFC). NFC is less complicated and gives great speed precision yet GA based PI controller produces significantly reduced torque and speed ripples compared with other controllers, in this way limiting losses in IM drives.

A Decoupling Estimation Scheme for Rotor Resistance and Mutual Inductance in Indirect Vector Controlled Induction Motor Drives

Coupling between the two parameter estimation loops, for rotor resistance and mutual inductance of an induction motor (IM), is presented and the resulting negative effects are discussed in this paper. To solve this problem, an online estimation scheme, estimating these two parameters in a decoupled way, is proposed for indirect vector controlled IM drives. The estimation scheme is implemented with a model reference adaptive system for the rotor flux. The reference model is taken by two sliding mode observers (SMOs) operating in series, i.e., a high-order terminal SMO, observing the defined back-electromotive force (EMF), and a first-order SMO, observing the rotor flux itself with the obtained back-EMF as its reference. Meanwhile, the current model of IM depending on both of these two parameters is chosen as the adaptive model. The error signal, driving each of the adaptive mechanisms, originates from the errors in magnitude and phase of the fluxes obtained in above two ways. The coupling between these two estimation loops are compensated by designing the weighting factors assigned to these two errors properly. Thus, the proposed scheme is decoupling, resulting in a significantly improved dynamic performance of the estimation. With the estimated parameter values, the slip speed can be calculated correctly and the field orientation can be achieved. Moreover, the torque tracking accuracy can be guaranteed. All of the analysis and design are validated through numerical simulations and laboratory experiments.

Dynamic controller design for an IVCIM

A dynamic output feedback (DOF) controller design is proposed for an indirect vector-controlled induction motor (IVCIM). The proposed controller is an alternative to the conventional proportional integral (PI) one and can be used for improving the dynamic performance of the IVCIM system. A sequential design method is used in which, first, the inner-loop controllers are designed. Then, the designed inner-loop controller is employed to design the outer speed-loop controller. The procedure incorporates the iron loss dynamics of the induction motor model to fetch the benefit of better performance than conventional controller designs neglecting the same. The DOF controller is designed in an H ∞ control framework by solving the iterative linear matrix inequality that makes stator currents and speed response to be robust against load disturbances and parameter variations. A first-order dynamic controller is tested to highlight its superiority over the PI controller. Comparison between the proposed dynamic and conventional PI controller is shown through several experimental results. It depicts enhanced performance by the proposed controller and suitability to industrial use.

Efficiency enhancement for indirect vector-controlled induction motor drive

ABSTRACTIn this paper, efficiency enhancement algorithms are developed and implemented on an indirect vector-controlled three-phase induction motor (IM) drive, and its performance under different operating conditions is analysed. The controllable electrical losses in the IM are minimised through the optimal control of direct axis (d-axis) stator current, and improvement in motor efficiency is achieved by weakening the rotor flux. The optimal d-axis stator current is also estimated using particle swarm optimisation (PSO) to validate the results obtained through analytical control method. The developed algorithms are tested under various operating conditions and the dynamic performance of the IM drive is analysed. The effectiveness of analytical and PSO-based efficiency optimisation control over conventional constant flux control, especially during light load at rated speed operation, is summarised. The effectiveness of the developed algorithm is validated experimentally through development of laboratory prototype set-up. The effect of parametric variation on efficiency, stator current, torque and speed of IM drive is studied through sensitivity analysis. The effect of variation in stator and rotor resistance due to change in operating temperature of the IM is also analysed and the robustness of the developed algorithm against parametric variations is demonstrated through simulation and experimental studies.

Concurrent PI Controller Design for Indirect Vector Controlled Induction Motor

AbstractIn this work, a concurrent proportional integral (PI) controller design technique is addressed for indirect vector controlled induction motor (IVCIM). For this, a full‐order induction motor (IM) model is employed that includes the stator current dynamics. The current‐loop proportional integral (PI) controllers are tuned to use the attributes of such dynamics. The robustness against load torque and rotor resistance variations are incorporated in the design problem. This method is implemented using a static output feedback scheme in which iterative linear matrix inequality (ILMI) based H∞ control technique is applied. The simulation and experimental comparison with existing designs show the superiority of proposed method and practical interest for industry use.

A Numerical Bifurcation Analysis of Indirect Vector-Controlled Induction Motor

Indirect vector-controlled induction motor (IVCIM) dynamics is well known for having different bifurcation behavior, viz., saddle-node bifurcation, Hopf bifurcation (HB), Bogdanov–Takens bifurcation, and zero-HB. A numerical analysis of these bifurcations for proportional–integral-controlled IVCIM is made in this brief using full-order IM model. Some new observations on the bifurcation behavior are made. Simulation and experimental results are presented validating the bifurcation behaviors.

PI controller design for indirect vector controlled induction motor: A decoupling approach

Decoupling of the stator currents is important for smoother torque response of indirect vector controlled induction motors. Typically, feedforward decoupling is used to take care of current coupling that requires exact knowledge of motor parameters, additional circuitry and signal processing. In this paper, a method is proposed to design the regulating proportional-integral gains that minimize coupling without any requirement of the additional decoupler. The variation of the coupling terms for change in load torque is considered as the performance measure. An iterative linear matrix inequality based H∞ control design approach is used to obtain the controller gains. A comparison between the feedforward and the proposed decoupling schemes is presented through simulation and experimental results. The results show that the proposed scheme is simple yet effective even without additional block or burden on signal processing.

Robust speed regulation of indirect vector control induction motor using fuzzy logic controllers based on optimization algorithms

Currently, in high-performance applications, the vector control (VC) scheme of induction motor (IM) is widely employed in industry. The VC scheme has significant features of decoupling torque and flux; also, its hardware implementation is easier. Conventionally, PID control schemes are frequently used for variable speed operation. However, the performance of the VC scheme is limited over a wide range of speed operation because of de-tuning caused by parameter uncertainties. To address the aforementioned challenging problem, adaptive and robust control strategies are mostly implemented. This paper presents various novel, adaptive, and robust control strategies, namely (a) fuzzy logic controller (FLC) based on Levenberg–Marquardt algorithm (LMA), (b) FLC based on steepest descent algorithm (SDA), (c) FLC based on Newton algorithm (NA), and (d) FLC based on Gauss–Newton algorithm (GNA) for the indirect vector control (IVC) three-phase IM. The focal motive is to accomplish fast dynamic response with fault-tolerant capability, load disturbance rejection qualities, insensitivity to the parameter uncertainties, robustness to speed variation, and to acquire maximum efficiency as well as torque. The \(d-q\) modeling of the IVC IM in the synchronous reference frame and space vector pulse width modulation (SVPWM) employed in inverter are designed in MATLAB/Simulink. Our work also presents critical, analytical, and comparative assessment of the proposed controllers with traditional tuned PI control strategy for the electrical faults perturbation, load disturbances, speed variations, and parameter uncertainties. Furthermore, the simulation results of the above-mentioned designed control strategies validated robust, smooth, and faster response with permissible overshoot, undershoot, settling time, and rise time for the IVC IM drive system, compared to prior works.

Comparative Study of Controller Design for Inverter Fed Indirect Vector Controlled Induction Motor Drive Systems

Abstract This paper presented a comparative study of speed controller design for inverter fed indirect vector controlled induction motor drive systems with symmetric optimum (SO) and genetic algorithm (GA) based model order reduction (MOR) techniques. GA was used in this paper for a new MOR and optimal time domain tuning of a Proportional Plus Integral (PI) controller. Comparative studies showed that the new model order reduction technique performed better than the conventional symmetric optimum order reduction technique. The performance of this controller was investigated with time domain specifications such as Overshoot, rise time, and settling time using the MATLAB-SIMULINK digital simulation software package. The simulated results showed that the proposed controller was better than that of the conventional PI controller.

An Intelligent Speed Controller Design for Indirect Vector Controlled Induction Motor Drive System

The aim of this paper is to present an Indirect vector control technique (Rotor- field Oriented) for a three phase Induction Motor drive using soft computing technique. Intelligent Speed Controller is being designed with Fuzzy Logic Controllers (FLC). Different Simulation experiments were carried out using MATLAB/SIMULINK to achieve at the best controller. The rules for the Fuzzy Controller were designed based on the dynamic behavior of the error signal. The performances of the proposed Fuzzy Logic Controller based Induction Motor drive are compared with that of the conventional PI controller based drive at different operating conditions.

IMPLEMENTATION OF PI, FUZZY & ANN CONTROLLERS TO IMPROVE DYNAMIC RESPONSE OF VECTOR CONTROLLED INDUCTION MOTOR DRIVE

Nowadays, vector controlled induction motor drives with variable speed applications are widely used in order to achieve good dynamic performance and wide speed control. In this paper a new method of controlling technique based on Artificial Neural Network is proposed to improve the speed control of indirect vector controlled induction motor drive. Indirect vector controlled induction motor with conventional PI controller is developed and is replaced with Fuzzy logic controller to overcome the problem of overshoot occurred in conventional PI controller. To obtain quick steady state response and better speed control, ANN technique is proposed and implemented using MATLAB/Simulink. In this paper the speed, torque and stator voltage responses with conventional PI controller, Fuzzy logic controller and proposed artificial neural network based controller are compared and found that the proposed ANN based controller showed increased dynamic performance.

Space Vector Pulse Width Modulation Based Indirect Vector Control of Induction Motor Drive

This paper presents the implementation of a different SPACE VECTOR PWM techniques applied to the indirect vector controlled induction motor (IM) drive involves decoupling of the stator current into torque and flux producing components of an induction motor. The drive control generally involves a fixed gain proportional-integral controller. Space vector pulse width modulation technique is widely used in inverter and rectifier controls. Compared to the sinusoidal PWM (SPWM), SVPWM is more suitable for digital implementation and can increase the obtainable maximum output voltage with maximum line voltage approaching 70.7% of the DC link voltage (compared to SPWM’s 61.2%) in the linear modulation range. This paper presents the indirect vector controlled induction motor drive using different Space Vector PWM techniques are implemented in MATLAB-SIMULINK. The corresponding harmonic spectrum is calculated for various PWM techniques and the results are compared.