Stator Resistance Variation Research Articles

Sensorless Torque/Speed Control of Induction Motor Drives at Zero and Low Frequencies With Stator and Rotor Resistance Estimations

Stability, robustness, and estimation accuracy of the adaptive flux observer (AFO) for sensorless induction motor (IM) drives are the most critical issues at zero and very low frequencies. In this paper, the design of speed, stator resistance, and rotor resistance estimators, to improve the robustness of AFO to parameters variation, is proposed. These estimators are arranged to have a cascade multi-input multi-output structure, and simplified to a single-input single-output structure for stability analysis and gain selections. To design both the observer feedback gains and adaptive proportional-integral gains, the stability conditions of the estimators are derived to guarantee a stable AFO in all the four quadrants of operation. The sensitivity analysis against stator and rotor resistance variations is also provided. The detailed analytical, simulation, and experimental results are presented to validate the proposed AFO of sensorless IM drives in torque- and speed-controlled modes of operation, particularly at zero and very low frequencies.

A novel NN based rotor flux MRAS to overcome low speed problems for rotor resistance estimation in vector controlled IM drives

This paper presents a new neural network based model reference adaptive system (MRAS) to solve low speed problems for estimating rotor resistance in vector control of induction motor (IM). The MRAS using rotor flux as the state variable with a two layer online trained neural network rotor flux estimator as the adaptive model (FLUX-MRAS) for rotor resistance estimation is popularly used in vector control. In this scheme, the reference model used is the flux estimator using voltage model equations. The voltage model encounters major drawbacks at low speeds, namely, integrator drift and stator resistance variation problems. These lead to a significant error in the estimation of rotor resistance at low speed. To address these problems, an offline trained NN with data incorporating stator resistance variation is proposed to estimate flux, and used instead of the voltage model. The offline trained NN, modeled using the cascade neural network, is used as a reference model instead of the voltage model to form a new scheme named as “NN-FLUXMRAS.” The NN-FLUX-MRAS uses two neural networks, namely, offline trained NN as the reference model and online trained NN as the adaptive model. The performance of the novel NN-FLUX-MRAS is compared with the FLUX-MRAS for low speed problems in terms of integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time absolute error (ITAE). The proposed NN-FLUX-MRAS is shown to overcome the low speed problems in Matlab simulation.

Backstepping flux observer for nonlinear control of the direct-drive permanent magnet synchronous generator wind turbines

This article described a backstepping flux observer and a backstepping control strategy applied to the wind energy conversion system based on synchronous generator. The proposed control technique is developed for both converters: the stator-side converter and the grid-side converter. The stator flux is recovered by the backstepping flux observer, and simultaneously, we introduce stator resistance variations to test the robustness of nonlinear backstepping control strategy. Then, MATLAB simulation works show high performances in terms of tracking, stability, and robustness against parameter variations.

Parallel Stator Resistance Estimator Using Neural Networks for Rotor Flux Based Model Reference Adaptive System Speed Observer

Model reference adaptive system schemes offer simpler implementation and require less computational effort compared to other speed sensorless methods. The performance of rotor flux based model reference adaptive system schemes at low-speed operation is poor because of parameter sensitivity and presence of the integrator in the reference model. As stator resistance inevitably varies with temperature, for accurate operation at low speeds, an appropriate online identification algorithm for the stator resistance is required. In this article, a neural network based parallel stator resistance and rotor speed estimator has been proposed to simultaneously rectify the limitation of model reference adaptive system schemes, i.e., stator resistance variation and DC offset due to integrator, employing a neutral network in stator resistance estimator and modifying the reference model by adding a compensating voltage term. An indirect sensorless vector control scheme has been simulated and experimentally validated using the dSPACE DS-1104 R&D controller board (dSPACE GmbH, Paderborn, Germany) to verify the performance of drives at different operating conditions.

Comparative assessment of two different model reference adaptive system schemes for speed‐sensorless control of induction motor drives

This study presents a comparison between a well-known instantaneous reactive power (Q)-based model reference adaptive system (MRAS) (Q-MRAS) and a relatively new X-MRAS used for the speed estimation of induction motor drive without speed sensor from the stability point of view. Stability aspects and sensitivity analysis of both the schemes to machine parameters variation are examined. The instantaneous and steady-state values of Q are considered to develop the reference and adaptive models respectively for the former, whereas those of a fictitious quantity X (i.e. * × ) are considered in the said models for the later. Non-computation of flux in these schemes leads to the satisfactory estimation of speed over a wide range maintaining the drive's stability in all the 4-quadrants. Both the Q- and X-MRAS are found to be dependent on the rotor resistance variation, whereas X-MRAS is also influenced by the stator resistance variation. A small signal stability study over a wide range of stator and rotor resistance variations is carried out to investigate the drive's stability in the low-speed region for both the MRAS schemes. Matlab/Simulink is used to obtain the simulation results for the proposed study. Experimental results are obtained through a dSPACE-1104-based laboratory prototype.

Design of Speed and Current Controllers Based on Online Particle Swarm Optimization Method for IPMSM Drives

In this paper, a novel online particle swarm optimization method is proposed to design speed and current controllers of vector controlled interior permanent magnet synchronous motor drives considering stator resistance variation. In the proposed drive system, the space vector modulation technique is employed to generate the switching signals for a two-level voltage-source inverter. The nonlinearity of the inverter is also taken into account due to the dead-time, threshold and voltage drop of the switching devices in order to simulate the system in the practical condition. Speed and PI current controller gains are optimized with PSO online, that means single irritation optimization computation can be completed within single sample time. In addition, the fitness function is changed according to the system dynamic and steady states. The proposed optimization algorithm is compared with conventional PI control method in the condition of step speed change and stator resistance variation, showing that the proposed online optimization method has better robustness and dynamic characteristics compared with conventional PI controller design.

An Adaptive Backstepping Flux Observer for two Nonlinear Control Strategies Applied to WGS based on PMSG

A nonlinear backstepping flux observer (BFO) for two control strategies applied to directly driven wind synchronous generator is proposed. A backstepping control (BC) and a sliding mode control (SMC) strategies are developed for both converters: the stator side converter (SSC) and the grid side converter (GSC). Using Lyapunov stability technique, one may guarantee the global stability of the wind energy conversion. The efficiency of the two non linear control strategies has been proved in terms of tracking and precision. Then, a comparative study between four possible combinations of control is illustrated and simulation results have shown high performances of the wind system controlled by BC strategy. Finally, stator resistance variation is introduced and it proves the robustness of the BC strategy applied to both converters with the BFO.

Robust software sensor with online estimation of stator resistance applied to WECS using IM

In this paper, a robust software sensor with online estimation of stator resistance applied to a wind energy conversion system (WECS) using an induction machine (IM) was developed. The mechanical speed control requires a specific estimation of the flux which is dependent on the IM stator resistance. As a consequence, this estimation may face some problems because of the stator resistance variation. In this paper, a robust software sensor was designed and developed for online stator resistance estimation under parametric variations. The estimated flux and mechanical speed as well as stator resistance are given by the software sensor. The robustness and stability study of the suggested software sensor were proven and validated by simulation results.

Torque Ripple Suppression Method for BLDCM Drive Based on Four-Switch Three-Phase Inverter

A novel inverter fault-tolerant control scheme is proposed to drive brushless DC motor. A fault-tolerant inverter and its three fault-tolerant schemes (i.e., phase A fault-tolerant, phase B fault-tolerant, and phase C fault-tolerant) are analyzed. Eight voltage vectors are summarized and a voltage vector selection table is used in the control scheme to improve the midpoint current of the split capacitors. A stator flux observer is proposed. The observer can improve flux estimation, which does not require any speed adaptation mechanism and is immune to speed estimation error. Global stability of the flux observer is guaranteed by the Lyapunov stability analysis. A novel stator resistance estimator is incorporated into the sensorless drive to compensate for the effects of stator resistance variation. DC offset effects are mitigated by introducing an integral component in the observer gains. Finally, a control system based on the control scheme is established. Simulation and experiment results show that the method is correct and feasible.

Online Voltage Sensorless High-Resistance Connection Diagnosis in Induction Motor Drives

A new method for online high-resistance connection (HRC) diagnosis in induction motor (IM) drives is presented in this effort. This voltage sensorless method is based on a signal injection strategy applied to the IM during its normal operation. The signal is injected in the $d$ -axis in such a way that it provides the phase currents with a dc component while avoiding torque perturbation. By measuring phase currents, it is possible to detect and isolate HRC problems. Experimental results showing high sensitivity to HRCs and immunity to symmetrical stator resistance variations have been obtained in a laboratory setup. The low perturbation produced by the injection method over the IM variables has also been experimentally demonstrated.

English

In this paper, a new method is proposed to estimate the Stator Resistance for direct torque control of Induction Motor. Direct Torque control of Induction Motor (IM) requires accurate estimation of torque, flux angle and resultant flux and these signals in turn depends on the motor flux. The flux is generally estimated using voltage model equations. The voltage model equation to a large extent depends on the accuracy of stator resistance of the induction motor. The stator resistance varies due to temperature during motor operation. Hence a method based on the active and reactive power is proposed for stator resistance estimation. The effect of stator resistance variation on the flux, torque and flux angle is studied and the results obtained are presented. The proposed method of stator resistance estimation is shown to track the various %changes in stator resistance with good accuracy.

Estimation of Stator Resistance in Direct Torque Control Synchronous Motor Drives

Direct torque control (DTC) is a high-performance method that provides effective control of stator flux modulus and electromagnetic torque of electric motors. However, error in estimation of stator resistance significantly degrades the performance of a DTC drive system, especially for a synchronous motor. This paper discusses the problems associated with the error in stator resistance and proposes an analytical approach to investigate its effect on the actual and estimated variables of a synchronous motor. Based on how this error affects the estimated angle between the stator flux and current vectors, a method to track stator resistance variations is proposed. The presented analytical method and the proposed stator resistance estimation are validated using simulation and experimental case studies.

A Robust Extended Kalman Filter for Speed-Sensorless Control of a Linearized and Decoupled PMSM Drive

This paper uses a robust feedback linearization strategy in order to assure a good dynamic performance, stability and a decoupling of the currents for Permanent Magnet Synchronous Motor (PMSM) in a rotating reference frame (d, q). However this control requires the knowledge of certain variables (speed, torque, position) that are difficult to access or its sensors require the additional mounting space, reduce the reliability in harsh environments and increase the cost of motor. And also a stator resistance variation can induce a performance degradation of the system. Thus a sixth-order Discrete-time Extended Kalman Filter approach is proposed for on-line estimation of speed, rotor position, load torque and stator resistance in a PMSM. The interesting simulations results obtained on a PMSM subjected to the load disturbance show very well the effectiveness and good performance of the proposed nonlinear feedback control and Extended Kalman Filter algorithm for the estimation in the presence of parameter variation and measurement noise.

Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network

In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.

Second-Order Sliding-Mode Observer With Online Parameter Identification for Sensorless Induction Motor Drives

Parameter identification plays an important role in speed estimation schemes. This paper presents a speed estimation scheme based on second-order sliding-mode supertwisting algorithm (STA) and model reference adaptive system (MRAS) estimation theory, in which both variations of stator resistance and rotor resistance are deliberately treated. A stator current observer is designed based on the STA, which is utilized to take the place of the reference voltage model of the standard MRAS algorithm. The observer is insensitive to the variation of rotor resistance and perturbation when the states arrive at the sliding mode. Derivatives of rotor flux are obtained and designed as the state of MRAS, thus eliminating the integration. Furthermore, in order to improve the near-zero speed operation, a parallel adaptive identification of stator resistance is designed relying on derivatives of rotor flux and stator current. Compared with the first-order sliding-mode speed estimator, the proposed scheme makes full use of the auxiliary sliding-mode surface, thus alleviating the chattering behavior without increasing the complexity. The robustness and effectiveness of the proposed scheme have been validated experimentally.

English

This paper proposes the controlling of Induction motor drives.Because of low maintenance and robustness, induction motors have many applications in industries. Speed control of induction motor is more important to achieve maximum torque and efficiency. Various control techniques such as scalar control, vector control, Sensor-less control are used. These Schemes suffers from parameter sensitivity and limited performance at low speed of operation. Sensor-less control of induction motor drive using model reference adaptive system with PI controller as reference model will limit the performance at low speed of operation. In this thesis, a novel adaptation mechanism is proposed which replaces PI controller in MRAS adaptation mechanism by a fuzzy logic controller. This is applied to a vector controlled drive and experimentally verified. This makes the reference model free from pure integration and less sensitive to stator resistance variations. This improves the performance of MRAS based sensor-less drives at low speed of operation.

High-Resistance Connection Detection in Induction Motor Drives Using Signal Injection

Two new methods for high-resistance connection (HRC) diagnosis in induction motor (IM) drives are presented in this effort. These automatic offline methods are based on a signal injection strategy applied to the IM at standstill. By measuring the voltage in the machine neutral point and phase currents, it is possible to detect and isolate connection problems. Experimental results showing a good sensitivity to HRCs and immunity to symmetrical stator resistance variations were obtained in a laboratory setup.

Modeling and Sensorless Direct Torque and Flux Control of a Dual-Airgap Axial Flux Permanent-Magnet Machine With Field-Weakening Operation

This paper presents the modeling and motion-sensorless direct torque and flux control of a novel dual-airgap axial-flux permanent-magnet machine optimized for use in flywheel energy storage system (FESS) applications. Independent closed-loop torque and stator flux regulation are performed in the stator flux ( x-y) reference frame via two PI controllers. This facilitates fast torque dynamics, which is critical as far as energy charging/discharging in the FESS is concerned. As FESS applications demand high-speed operation, a new field-weakening algorithm is proposed in this paper. Flux weakening is achieved autonomously once the y-axis voltage exceeds the available inverter voltage. An inherently speed sensorless stator flux observer immune to stator resistance variations and dc-offset effects is also proposed for accurate flux and speed estimation. The proposed observer eliminates the rotary encoder, which in turn reduces the overall weight and cost of the system while improving its reliability. The effectiveness of the proposed control scheme has been verified by simulations and experiments on a machine prototype.

Modeling and Experimental Verification of ANN Based Online Stator Resistance Estimation in DTC-IM Drive

Direct Torque controlled induction motor (DTC-IM) drives use stator resistance of the motor for stator flux estimation. So, stator resistance estimation properly is very important for a stable and effective operation of the induction motor. Stator resistance variations because of changing in temperature make DTC operation difficult mainly at low speed. A method based on artificial neural network (ANN) to estimate the stator resistance online of IM for DTC drive is modeled and verified in this paper. To train the neural network a back propagation algorithm is used. Weight adjustment of neural network is done by back propagating the error signal between measured and estimated stator current. An extensive simulation has been carried out in MATLAB/SIMULINK to prove the efficacy of the proposed stator resistance estimator. The simulation & experimental result reveals that proposed method is able to obtain precise torque and flux control at low speed.

Sliding-Mode Observer based Direct Torque Control of an IPMSynchronous Motor Drive at Very Low Speed

Fast torque and flux dynamic responses are delivered by direct torque control of interior permanentmagnet synchronous motors. However, the major drawback is being the poor flux estimation at very low speeds. A majority of flux observers have been proposed for flux estimation, but most of them fail to check in the lowspeed region. In this paper, to improve flux estimation at very low speeds, a sliding-mode stator flux observer is proposed. This observer does not require any speed adaptation mechanism unlike conventional flux observers and is immune to speed estimation error. A novel stator resistance estimator is incorporated into the sensor less drive to compensate the effects of stator resistance variation. DC-offset effects are reduced by introducing a small elemental component in the observer gains. Simulation results at very low speeds, including 0 and 2 r/min, without signal injection confirm the effectiveness of the proposed method.