Value Of Rotor Resistance Research Articles

Robust Control Facing a Large Scale Parameter Variation in a Sensorless Vector Controlled Induction Motor Drive

In vector control systems using the slip frequency control method, the rotor resistance of induction motors is used to calculate a slip frequency. Thus the change in temperature and saturation levels cause the variation of rotor resistance value and hence the deterioration of the whole torque control characteristic. This paper presents a simulation of induction motor behavior facing a sudden parameter variation in very low speed basing on fuzzy logic perturbation rejection controllers, the rotor flux and speed are observed by an adaptive Luenberger observer based fuzzy adaptation mechanism. Simulation results show that the proposed control with the proposed fuzzy regulation provide good performance dynamic characteristics for a large scale parameter variation. All simulations have been realized in MatLab/Simulink.

The full-order state observer speed-sensorless vector control based on parameters identification for induction motor

During the operation of speed-sensorless control system for induction motor, the stator and rotor resistance varies greatly with the change of temperature and the frequency of the rotor side, which affects the estimation of the stator flux and leads to the low accuracy of the speed estimation. A speed-sensorless vector control method based on parameters identification with the full-order adaptive state observer is proposed in this paper. In the model reference adaptive system of AC motor, the stator resistance and rotor flux are assigned as state variables to build the reference model, and a full-order flux observer is introduced to adjustable model. Lyapunov theory and Popov superstability theory are used to deduce the speed and rotor resistance adaptive rate. The feedback gain matrix is simplified to speed up the convergence rate of the system. The estimation values of speed and rotor resistance are taken as the proportional integral form, so that an interactive model reference adaptive system is constructed by speed and rotor resistance identification. While observing the rotor flux, it can not only ensure the accuracy of the reference model but also eliminate the disadvantages of the voltage model with integral terms, and the rotor speed can be estimated at the same time. The experimental results show that the accurate performance of speed and flux identification can meet the requirements of application; the proposed control method with the identification of speed and rotor resistance has little fluctuations phenomenon on motor torque in low speed and achieves better performance.

New-fangled Technique for Fault Tolerant Control

In this paper Descending Viewer Method (DVM) projected for finding and fault tolerant control of stator inter-turn short circuit faults in doubly-fed induction generators based in wind turbine. A process has been developed that allows the way from ostensible controllers designed for strong condition, to vigorous controllers designed for defective condition. Finally value of the rotor resistance estimated & is based on the use of the error between real and probable value of doubly fed induction generator (DFIG) in faulty condition, this will perk up the performance of this viewer. Simulation results show the reliability of the proposed Descending Viewer Method (DVM) approach.

New Version of Adaptive Speed Observer based on Neural Network for SPIM

This paper presents a novel Stator Current based Model Reference Adaptive System (SC_MRAS) speed observer for high-performance Six Phases Induction Motor (SPIM) drives using linear neural network. The article aim is intended to improve performance of an SC_MRAS observer, which were presented in the literature. In this proposed scheme, the measured stator current components are used as the reference model of the MRAS observer to avoid the use of a pure integrator and reduce the influence of motor parameter variation. The adaptive model uses a two-layer Neural Network (NN) to estimate the stator current, which has been trained online by means of a Least Squares (LS) algorithm instead of uses a nonlinear Back Propagation Network (BPN) algorithm to reduce the complexity and computational burden, it also help to improve some disadvantages cause by the inherent nonlinearity of the BPN algorithm as local minima, two heuristically chosen parameters, initialization, and convergence problems, paralysis of the neural network. The adaptive model of the proposed scheme is employed in prediction mode, not in simulation mode as is usually the case in the literature, this made the proposed observer operate better accuracy and stability. In the proposed observer, stator and rotor resistance values are estimated online, these values thereafter were updated for the current observer and rotor flux identifier to enhance the accuracy, robustness and insensitivity to parameters variation for the proposed observer. The proposed LS SC_MRAS observer has been verified thought the simulation and compared with the BPN MRAS observer. The simulation results have proven that the speed is estimated a consequent quicker convergence, do not need the estimated speed filter, lower estimation errors both in transient and steady state operation, better behavior in low and zero speed operation.

Diagnosis of the Induction Machine Using Advanced Signal Processing Methods

This work is a part of the thematic of monitoring and fault diagnosis of the squirrel cage three-phase induction machine. The choice of this type of machine is justified by the growing success it has exhibited, mainly, in the electric drives with variable speed. Signal based detection methods are presented is validated in simulation. The proposed diagnosis approach requires only little experimental data, and more importantly it provides efficient simulation tools that allow characterizing faulty behavior.In this study, the proposed approach considers the value of rotor resistance as fixed for condition monitoring. This value in the diagnostic tools which one uses is not fixed contrary to the classical approaches of control of machine.

Sensorless Control of IM Based on Stator-Voltage MRAS for Limp-Home EV Applications

This paper proposes a novel sensorless speed estimation for an induction motor (IM) based on a new stator-voltage model reference adaptive system (Vs-MRAS) scheme. This is utilized for torque-controlled drive (TCD) based on indirect rotor field oriented control technique in limp-home mode operation of electric vehicles (EV) applications. The Vs-MRAS scheme uses the error between the reference and estimated stator-voltage vectors and estimates the synchronous speed. Unlike existing MRAS schemes, the proposed sensorless scheme does not require the measured nominal values of stator resistance, stator inductance, and rotor resistance. This scheme is insensitive to variations of the aforementioned parameters. Moreover, using the proposed scheme eliminates the need for slip calculation. The proposed scheme is implemented and experimentally tested in a lab environment, on a 19-kW IM, and also applied on an electric golf buggy, powered by a 5-kW IM. The experimental results show that the proposed scheme is immune to parameter variations and is consistent in vehicle-starting from standstill and hill-starting tests. This scheme is also free from drift problems associated with a pure integration and is stable in the field weakening region. The test-drive results from the golf buggy confirm suitability of the proposed Vs-MRAS scheme over a wide range of speeds for the purpose of TCD in EV applications.

Maximizing Output Power in Oscillating Water Column Wave Power Plants: An Optimization Based MPPT Algorithm

This paper proposes an optimization based maximum power point tracking (MPPT) algorithm for selection of appropriate external rotor resistances of wound rotor induction generator (WRIG). The generator coupled with a Wells turbine is used in oscillating water column (OWC) wave power plants. The Wells turbine suffers with stalling behaviour that reduces the average output power significantly. Hence, the objective is to prevent aerodynamic stalling of the Wells turbine and hence maximizing the output power simultaneously. But inappropriate selection of rotor resistance leads to excessive power loss. Therefore, two approaches have been applied: (i) the rotor resistance values are selected manually by trial and error method or non-optimized approach (ii) a performance index has been derived and minimized using particle swarm optimization technique to obtain the optimized values of rotor resistance. Simulation results have been performed for turbine efficiency, output power and power loss in external rotor resistance. Finally, the proposed control approach is illustrated for two particular cases of regular and irregular waves.

Real time implementation of indirect rotor flux oriented control of a five-phase induction motor with novel rotor resistance adaption using sliding mode observer

High performance multiphase motor drive requires precise knowledge of the state quantities and the machine parameters. Access to these state quantities is through measurement using sensors whose accuracy is paramount to achieve the performance level required by industrial applications. However, the problems of the parameters variations, inaccessibility to the measurement of some states, no-observability of the machine in some regions, the cost of the sensors and their lack of precision, make this very difficult task. To address these problems, it is necessary to resort to soft sensors through the design of observers and estimators. In multiphase induction motor drive, the observation problem arises especially for rotor flux that is not accessible for measurement. About the parameters variations, the rotor resistance and the stator resistance are the most critical parameters of the machine because their influence is crucial for the control and observation. The change in the resistances can be as large as 40–50% of the rated value, which may affect the control adversely. This paper develops a new structure of an adaptive sliding mode observer based on an online estimation of the rotor resistance value in order to avoid the effect of its variation in the rotor flux oriented control. The results show convergent (the errors in the transient and steady states are 5% and 1%, respectively) behavior of the drive using the proposed control scheme for large rotor resistance variation under loaded condition. The stability of the drive is proved using Lyapunov criteria. The simulation results are validated using real time implementation.

Induction Motor Faults Detection Using a Statistical Procedure Based-Approach

This paper presents the application of a new technique based on the variance of three phase stator currents’ instantaneous variance (VIV-TPSC) to detect faults in induction motors. The proposed fault detection algorithm is based on computation of the confidence interval index (CI) at different load conditions. This index provides an estimate of the amount of error in the considered data and determines the accuracy of the computed statistical estimates. The algorithm offers the advantage of being able to detect faults, particularly broken rotor bars, independently of loading conditions. Moreover, the implementation of the algorithm requires only the calculation of the variance of the measured three-phase stator currents’ instantaneous variance. The discrimination between faulty and healthy operations is based on the adherence of VIV-TPSC value to the CI which is calculated after checking out that the variance of instantaneous variance is a random variable obeying to normal distribution law. Rotor and stator resistance values are not used in any part of the CI and VIV-TPSC calculations, giving the algorithm more robustness. The effectiveness and the accuracy of the proposed approach are shown under different faulty operations.

Online Adaptation of Rotor Resistance based on Sliding Mode Observer with Backstepping Control of A Five-Phase Induction Motor Drives

Multiphase electric drives have been developed due to numerous advantages offered by those machines when it compared with the conventional three-phase machines. Multiphase motor drives are considered for applications, where the reduction of power per phase for both motor and inverter and high reliability are required. High performance control techniques are developed for multi-phase drives. The performances of the high performance controller and flux observers may be degraded during the operation. Since the parameters of Induction Motor (IM) varies continuously due to temperature variation and heating. Thus it is significantly important that the value of rotor resistance is continuously observed online and adapted by the control algorithm in order to avoid detuning effects. The efficiency and performance of an induction motor drive system can be improved by online observation of the critical parameters, such as the rotor resistance and stator resistance. Among the parameters of IM, rotor resistance is a decisive one for flux estimation, and also the stator resistance becomes critical in the low-speed operation condition. This paper presents a new online estimation method for the rotor resistance of the IM for sliding mode observer. This method generally based on theories of variable structure and is useful in order to adjust online unknown parameters (load torque and rotor resistance). The presented non-linear compensator afford a voltage inputs on the articulation of stator current and rotor speed measurements, and engender an estimates for the unknown parameters simultaneously, the non-measurable state variables (rotor flux and derivatives of the stator current and voltage) that converge to the corresponding true values. Under the persistent excitation condition, the proposed method estimates the actual value of rotor resistance, which guarantees the exact estimation of the rotor flux. Non-linear Backstepping control and adaptive sliding mode observer of a five-phase induction motor drive is presented. The accuracy and validity of the method is verified by MATLAB simulation model.

Difference in maximum torque-speed characteristics of induction machine between motor and generator operation modes for electric vehicle application

This paper explains the reason for the difference in maximum torque-speed characteristics and power factor of IM between the motor and generator operation modes and investigates the influence of machine design parameters on the difference, such as stator resistance, rotor resistance and iron loss etc. An analytical mathematical model based on the dq reference frame is employed for investigation. It is shown that the difference in torque-speed characteristics between the motor and generator operation modes is mainly caused by the opposite direction of q-axis current and affected by stator and rotor resistance values. Finally, analytically calculated torque-speed characteristics of a 12kW IM in the motor and generator operation modes are verified by finite element analyses.

Estimating rotor resistance of wound rotor induction motor by look-up table and fuzzy logic approach

Wound rotor induction motors are commonly used in high torque, adjustable speed industrial applications. The provision to add external resistances to the rotor offers high starting torque and smooth starting at a low starting current. In this paper, the look-up table method and fuzzy logic methods are introduced to estimate the rotor resistance value. This is achieved by controlling the firing angle of the thyristor converter circuit connected to the rotor. The smooth variation in speed with constant starting current and high torque is achieved for various reference values. The performance of the look-up table method and fuzzy logic controller method are compared with the traditional external resistance method. The results show that the speed error in the look-up table method is higher than in the fuzzy logic method but less than the traditional external resistance method. The performance of the fuzzy logic method is closer to the traditional external resistance method with less speed error and settling time.

Adaptive fuzzy self-learning controller based rotor resistance estimator for vector controlled induction motor drive

This paper presents an intelligent approach to identify and adapt the rotor resistance for an indirect vector controlled induction motor drive. This command is affected by rotor resistance; the variation of this parameter could distort the decoupling between flux and torque and, consequently, lead to deterioration of drive performance. To overcome this problem, a fuzzy estimator is provided to identify the real value of rotor resistance in order to obtain a vector control optimal. Then we propose a fuzzy adaptive control strategy fits into the learning methods context by modifying the consequences of fuzzy estimator. Regarding the learning algorithm, our solution envisages the use of a fuzzy inverse model, combined with a mechanism that acts based on estimator rules by modifying the consequents according to a certain criterion, so as to increase the system robustness, and avoid unnecessary oscillation in the control signal. The suggested rotor resistance identification approach has been validated by simulation study.

An online estimator for rotor resistance in vector drives of induction machines based on Walsh functions

In a modern electrical driver, rotor field oriented control (RFOC) method has been used to achieve a good performance and an appropriate transient response. In this method, the space vector of the rotor flux comes handy by the rotor resistance value. The rotor resistance is one of the important parameters which varies according to motor speed and room temperature alteration. In this paper, a new on-line estimation method is utilized to obtain the rotor resistance by using Walsh functions domain. The Walsh functions are one of the most applicable functions in piecewise constant basis functions (PCBF) to solve dynamic equations. On the other hand, an integral operational matrix is used to simplify the process and speed of the computation algorithm. The simulations results show that the proposed method is capable of solving the dynamic equations in an electrical machine on a time interval which robustly estimates the rotor resistance in contrast with injection noises.

Conditioning of a statistical indicator for the detection of an asynchronous machine rotor faults

In this paper, the cyclostationary characteristics of electrical signals will be exploited in order to detect the rotor faults of an asynchronous machine. These defects are the most complex in terms of detection since they interact with the 50 Hz carrier with a weak band occupied in frequency. The testing ground used includes an industrial three-phase wound rotor asynchronous motor of 400 V, 6.2 A, 50 Hz, 3 kW, 1385 rpm characteristics. The rotor fault has been carried out by adding an extra 40 mΩ resistance on one of the rotor phases (i.e. 10% of the rotor resistance value per phase, R r = 0.4Ω). From the stator voltage and current acquisition, and by application of the Time Synchronous Averaging (TSA) method to the stator current, the electrical signal will be conditioned in order to obtain a sensitive indicator allowing to easily distinguish the healthy cases from defective ones.

Research on Starting Process of a Novel Wound Rotor Line-Start Permanent Magnet Synchronous Motor

Squirrel-cage rotor line-start permanent magnet synchronous motor can not meet the heavy lifting, frequent starting conditions for the lifting requirements. In order to solve the difficult problem of starting caused by the squirrel-cage rotor line-start permanent magnet synchronous motor (LS-PMSM), a novel wound rotor line-start permanent magnet synchronous motor is proposed in this paper, which uses wound rotor to replace squirrel cage rotor. In this paper, the finite element analysis method is adopted to simulate and analyze the starting process of the motor, through reasonably determining the value of the external rotor resistance, the starting performance of the motor has improved greatly, and lead the motor smoothly into synchronous speed. The simulation results show the correctness and effectiveness of the research methods.

An improved rotor resistance estimator for induction motors adaptive control

AbstractAn online rotor resistance estimator for induction motor adaptive control is presented. The rotor resistance scheme uses the rotor speed, the stator current, voltage provided by the controller, time-derivatives of the stator current and voltage. The time-derivatives of the stator current and voltage are estimated using 2nd order sliding mode observer. The finite time convergence of the estimated rotor resistance to the nominal value is achieved when the classical persistent excitation condition is satisfied by the input signal. The method is suitable for online operation to continuously update the rotor resistance value. Experimental results with online variation of the rotor resistance show that the proposed algorithm gives better performance compared to the previous work. The other interesting feature of the proposed method is the robustness properties with respect to the variation of the stator resistance both at relatively high speed and low speed.

Detection of broken rotor bars in induction motors using nonlinear Kalman filters

This paper presents a model-based fault detection approach for induction motors. A new filtering technique using Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) is utilized as a state estimation tool for on-line detection of broken bars in induction motors based on rotor parameter value estimation from stator current and voltage processing. The hypothesis on which the detection is based is that the failure events are detected by jumps in the estimated parameter values of the model. Both UKF and EKF are used to estimate the value of rotor resistance. Upon breaking a bar the estimated rotor resistance is increased instantly, thus providing two values of resistance after and before bar breakage. In order to compare the estimation performance of the EKF and UKF, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. Computer simulations are carried out for a squirrel cage induction motor. The results show the superiority of UKF over EKF in nonlinear system (such as induction motors) as it provides better estimates for rotor fault detection.

Using an Artificial Neural Network as a Rotor Resistance Estimator in the Indirect Vector Control of an Induction Motor

This paper presents a rotor resistance estimator based on an artificial neural network (ANN) used in the indirect vector control (IVC) of an induction motor (IM). Attention is focused on the dynamic performance of ANN rotor estimator, which gives superior performance over the fuzzy logic based rotor estimator reported in technical literature. The simulation was done using a 1.5 HP induction motor. The same ANN rotor estimator was proved with other IM having different rated power. The use of the same ANN was possible because the scaling and descaling (normalization) of the input and output of ANN was property done for each motor. The ANN training was done offline using the Levenberg-Marquardt algorithm. The neuronal network is a three-layer network; the first layer has fourteen neurons (or nodes), the hidden layer has five neurons and the output layer has only one neuron because the unique output signal is the rotor resistance value.

Sliding-Mode Flux Observer With Online Rotor Parameter Estimation for Induction Motors

Field orientation techniques without flux measurements depend on the parameters of the motor, particularly on the rotor resistance or rotor time constant (for rotor field orientation). Since these parameters change continuously as a function of temperature, it is important that the value of rotor resistance is continuously estimated online. A fourth-order sliding-mode flux observer is developed in this paper. Two sliding surfaces representing combinations of estimated flux and current errors are used to enforce the flux and current estimates to their real values. Switching functions are used to drive the sliding surfaces to zero. The equivalent values of the switching functions (low-frequency components) are proven to be the rotor resistance and the inverse of the rotor time constant. This property is used to simultaneously estimate the rotor resistance and the inverse of the time constant without prior knowledge of either the rotor resistance or the magnetizing inductance. Simulations and experimental results prove the validity of the proposed approach