Model Reference Adaptive System Speed Estimator Research Articles

Advanced sliding mode speed observer with compensated stray‐load and iron losses for a stand‐alone wind turbine‐driven induction generator

AbstractModel‐based speed estimation schemes in sensorless field‐oriented control (FOC) of induction machines (IMs) are designed on the basis of a corresponding mathematical model. Thus, a reliable and robust sensorless controller requires using a more detailed IM model. The omission of nonlinear phenomena in real‐time application, such as magnetic saturation, iron losses, and stray‐load losses, certainly leads to inaccuracy in the estimated speed due to the existence of the mentioned phenomena in the IM. In this context, we address here the problem of neglecting the stray‐load and iron losses in a model‐based speed observer, synthesized by means of sliding mode theory, and we evaluate the impact of this omission on the execution of the utilized FOC and maximum power point tracking (MPPT) algorithm applied for a stand‐alone wind energy conversion system (WECS). The performance of the proposed sliding mode speed observer (SMSO), derived from the IM model including the above mentioned nonlinear phenomena, is experimentally evaluated over wide ranges of wind speed and IM magnetizing flux. It is also compared with the model reference adaptive system (MRAS) speed estimator, which is derived from the same IM model, as well as to the simplified SMSO, designed based on the IM model without stray‐load and iron losses. The obtained results show that the proposed SMSO has greater robustness with respect to abrupt variations of the rotor flux in comparison with the MRAS speed estimator, whereas it ensures significantly more stable operation and lower speed estimation error in comparison with the simplified SMSO.

Stator Current-Based Model Reference Adaptive Control for Sensorless Speed Control of the Induction Motor

This paper described that the stator current-based model reference adaptive system (MRAS) speed estimator is used for the induction motor (IM) indirect vector speed control without a mechanical speed sensor. Due to high sensitivity of motor parameters variation at low speed including zero, stability analysis of MRAS design is performed to correct any mismatch parameters value in the MRAS performed to estimate the motor speed at these values. As a result, the IM sensorless control can operate over a wide range including zero speed. The performance of the stator current-based MRAS speed estimator was analyzed in terms of speed tracking capability, torque response quickness, low speed behavior, step response of drive with speed reversal, sensitivity to motor parameter uncertainty, and speed tracking ability in the regenerative mode. The system gives a good performance at no-load and loaded conditions with parameter variation. The stator current-based MRAS estimator sensorless speed control technique can make the hardware simple and improve the reliability of the motor without introducing a feedback sensor, and it becomes more important in the modern AC IM. The sensorless vector control operation has been verified by simulation on Matlab and experimentally using Texas Instruments HVMTRPFCKIT with TMS320 F28035 DSP card and 0.18 kw AC IM.

Speed-Sensorless Vector Control of an Induction Generator Including Stray Load and Iron Losses and Online Parameter Tuning

Field-oriented control (FOC) is standardly used in high-performance induction machine (IM) applications. However, the performance of FOC algorithms strongly depends on the level of agreement between the actual IM and the respective model used for the algorithm design. IM parameters' values are known to vary with the operating conditions, so their online identification is preferable. In addition, it is often preferred to acquire the rotor speed by estimation because speed sensors increase the cost and size of the system while reducing its reliability. Model-reference-adaptive-system (MRAS) estimators are popular due to their simple implementation, but they are sensitive to IM parameters and, hence, usually include a parameter tuning mechanism. This article proposes an indirect rotor-field-oriented controller with the parallel MRAS speed estimation and IM parameter tuning - implemented in an induction generator system. Both the control algorithm and the MRAS include the stray load and iron losses, magnetic saturation, and rotor resistance variation, whereas the MRAS additionally accounts for variations of the stator and stray-load resistances. The system performance is experimentally evaluated over wide ranges of the IM flux, speed, torque, and temperature.

Laboratory implementation of electromagnetic torque based MRAS speed estimator for sensorless SMPMSM drive

This Letter proposes a simple electromagnetic torque based model reference adaptive system (MRAS) speed estimator for sensorless surface mount permanent magnet synchronous motor (SMPMSM) drive. The proposed estimator is formed using instantaneous measured and estimated electromagnetic torque. The proposed estimator is implemented for 1.5 kW laboratory prototype SMPMSM drive using field programmable gate array ALTERA cyclone II. Experimental results demonstrate the efficacy of the proposed scheme under different test conditions viz. different low-speed regions and standstill at different load conditions with uncertainty in machine parameters. Results show that the estimator is stable in low-speed regions and exhibits robustness against uncertainties in machine parameters.

Robust Speed Controller Design Using H_infinity Theory for High-Performance Sensorless Induction Motor Drives

In this paper, a robust speed control scheme for high dynamic performance sensorless induction motor drives based on the H_infinity (H) theory has been presented and analyzed. The proposed controller is robust against system parameter variations and achieves good dynamic performance. In addition, it rejects disturbances well and can minimize system noise. The H controller design has a standard form that emphasizes the selection of the weighting functions that achieve the robustness and performance goals of motor drives in a wide range of operating conditions. Moreover, for eliminating the speed encoder—which increases the cost and decreases the overall system reliability—a motor speed estimation using a Model Reference Adaptive System (MRAS) is included. The estimated speed of the motor is used as a control signal in a sensor-free field-oriented control mechanism for induction motor drives. To explore the effectiveness of the suggested robust control scheme, the performance of the control scheme with the proposed controllers at different operating conditions such as a sudden change of the speed command/load torque disturbance is compared with that when using a classical controller. Experimental and simulation results demonstrate that the presented control scheme with the H controller and MRAS speed estimator has a reasonable estimated motor speed accuracy and a good dynamic performance.

Speed Estimation Technique Using Modified Stator Current Error-Based MRAS for Direct Torque Controlled Induction Motor Drives

This paper uses a stator current error-based model reference adaptive system (MRAS) to estimate the speed of an induction motor drive. Usually, speed error signal uses the cross product of stator current error vector and flux linkage vector for the estimation of speed. However, this speed error signal has limitations, such as speed tracking error and instability (at regenerative mode) under loaded conditions at low speeds. To mitigate these issues, a modified MRAS speed estimation algorithm is proposed in this paper. In the proposed method, the adaptive model state variables are formulated in terms of flux linkages (stator and rotor) in order to alleviate the impact of current noise in the flux linkage estimation. Moreover, a compensator is added to reduce the speed error tracking in the reference model. Furthermore, the stability of the drive is kept by using the dot product of stator current error vector and the rotor flux linkage vector during the regenerative mode of operation. The analysis of the proposed algorithm is presented and validated experimentally on the laboratory setup with direct torque control-space vector modulation technique for a wide operating range including the dynamic performance.

MRAS Based Speed Estimator for Sensorless Vector Control of a Linear Induction Motor with Improved Adaptation Mechanisms

This paper deals with model reference adaptive system (MRAS) speed estimators based on a secondary flux for linear induction motors (LIMs). The operation of these estimators significantly depends on an adaptation mechanism. Fixed-gain PI controller is the most common adaptation mechanism that may fail to estimate the speed correctly in different conditions, such as variation in machine parameters and noisy environment. Two adaptation mechanisms are proposed to improve LIM drive system performance, particularly at very low speed. The first adaptation mechanism is based on fuzzy theory, and the second is obtained from an LIM mechanical model. Compared with a conventional PI controller, the proposed adaptation mechanisms have low sensitivity to both variations of machine parameters and noise. The optimum parameters of adaptation mechanisms are tuned using an offline method through chaotic optimization algorithm (COA) because no design criterion is given to provide these values. The efficiency of MRAS speed estimator is validated by both numerical simulation and real-time hardware-in-the-loop (HIL) implementations. Results indicate that the proposed adaptation mechanisms improve performance of MRAS speed estimator.

Sensorless Indirect Field Oriented Control of Single-sided Linear Induction Motor With a Novel Sliding Mode MRAS Speed Estimator

This paper proposes a new sliding mode control (SMC) based model reference adaptive system (MRAS) for sensorless indirect field oriented control (IFOC) of a single-sided linear induction motor (SLIM). The operation of MRAS speed estimators dramatically depends on adaptation mechanism. Fixed-gain PI controller is conventionally used for this purpose which may fail to estimate the speed correctly in different condition such as variation in the machine parameters. In this paper, PI controller is replaced by SMC based adaptation mechanism. One of the major problems of SMC is high-frequency chattering due to switching control function. In order to alleviate this problem, a new switching control function is presented. The SMC parameters are tuned using an off-line method through chaotic optimization algorithm (COA) because no design criterion is given to provide these values. The operation of sliding mode MRAS speed estimators (SM-MRAS) is validated by numerical simulation. The simulation results confirm the efficiency of proposed adaptation mechanism.

Type-2 fuzzy logic control based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive

In this research study, a model reference adaptive system (MRAS) speed estimator for speed sensorless direct torque and flux control (DTFC) of an induction motor drive (IMD) using two adaptation mechanism schemes are proposed to replace the conventional proportional integral controller (PIC). The first adaptation mechanism scheme is based on Type-1 fuzzy logic controller (T1FLC), which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type-1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties presents in the system which can be caused by sudden change in speed or different load disturbances, process noise etc. Therefore, a new Type-2 fuzzy logic controller (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties and improves the performance and also robust to various load torque and sudden change in speed conditions, respectively. The detailed performances of various adaptation mechanism schemes are carried out in a MATLAB/Simulink environment with a speed sensor and speed sensorless modes of operation when an IMD is operating under different operating conditions, such as, no-load, load and sudden change in speed, respectively. To validate the different control approaches, the system also implemented on real-time system and adequate results are reported for its validation.

Sensor less Operation of CSI Fed Induction Motor Drive by Using MRAS

The underground mines, oil and gas industries need remote operation of vector controlled medium voltage variable speed induction motor drives with long feeder. The usage of voltage source inverters in such cases leads to motor over voltage and harmonic problems. The current source inverter (CSI) has no such problems to these applications because of its motor friendly voltages output. This paper proposes sensor less operation of a CSI fed induction motor drive and improves the speed estimation. The proposed model uses model reference adaptive system (MRAS) speed estimator requires motor voltage and currents as inputs. Direct measurement of motor voltages and currents are impractical for long feeder drives due to feeder length and safety of environmental precautions. Hence the motor voltages and currents are drawn from the inverter out. In this paper the flux estimation needed for MRAS speed estimation is proposed to observe the performance of the induction motor with sudden change of loads under steady state. This model is simulated in The model reference adaptive system contains two models they are stator model and rotor model. The speed of the machine is estimated by using the above two models (2). The inputs required for the model reference adaptive system is the voltages and currents. The speed is estimated from the calculation of rotor flux linkages in the stationary reference frame from the both stator and rotor models. The estimated speed is calculated from the error obtained by cross multiplication of the flux linkages produced in the two models (stator and rotor models) with PI controller. The estimated speed is fed back to the one of the input of rotor model. Rotor model is also called as adaptive or adjustable model and the stator model is called the reference model. The two models are designed in Simulink by using the following equations and the block diagram is represented as shown in fig.1. The equations of reference model as follows

Improved Sensorless Operation of a CSI-Based Induction Motor Drive: Long Feeder Case

Various applications, like in underground mines and oil and gas industries, require remote operation of vector-controlled medium-voltage variable speed drives via a long motor feeder. The use of voltage source inverters in such cases leads to motor overvoltage and harmonic quality problems. The current source inverter (CSI) is ideally matched to these applications because of its motor-friendly voltage output. Speed sensorless operation is mandatory due to the long motor feeder. Although the model reference adaptive system (MRAS) is a powerful and proven speed estimation tool, its implementation in long motor feeder drives faces many challenges. Among them, and addressed in this paper, are inherent dc offset in its stator model, the need for actual motor voltage and current values, and oscillations in the estimated speed due to errors in the motor current measurement signals. In this paper, a sensorless CSI vector-controlled drive, suitable for long motor feeder applications, is studied. Improved speed estimation is achieved by proposing 1) a modified dc-offset eliminator for an MRAS speed estimation and 2) a compensation technique for motor current's measurement errors. Intensive experimental results, for a low-voltage scaled model, along with simulations validate the effectiveness of the proposed technique.

Neural learning adaptive system using simplified reactive power reference model based speed estimation in sensorless indirect vector controlled induction motor drives

Abstract This paper presents a novel speed estimator using Reactive Power based Model Reference Neural Learning Adaptive System (RP-MRNLAS) for sensorless indirect vector controlled induction motor drives. The Model Reference Adaptive System (MRAS) based speed estimator using simplified reactive power equations is one of the speed estimation method used for sensor-less indirect vector controlled induction motor drives. The conventional MRAS speed estimator uses PI controller for adaptation mechanism. The nonlinear mapping capability of Neural Network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. This paper proposes the use of neural learning algorithm for adaptation in a reactive power technique based MRAS for speed estimation. The proposed scheme combines the advantages of simplified reactive power technique and the capability of neural learning algorithm to form a scheme named “Reactive Power based Model Reference Neural Learning Adaptive System” (RP-MRNLAS) for speed estimator in Sensorless Indirect Vector Controlled Induction Motor Drives. The proposed RP-MRNLAS is compared in terms of accuracy, integrator drift problems and stator resistance versions with the commonly used Rotor Flux based MRNLAS (RF-MRNLAS) for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated.

Design and implementation of sensorless DC inverter-fed compressor drive system

A novel proportional–integral–derivative neural network (PIDNN) controller is proposed to replace the PI controller, which is adopted in conventional high-frequency signal injection to improve the speed estimating performance for start-up and low speeds of the interior permanent magnet synchronous motor (IPMSM) used in DC inverter-fed compressor drive systems. Moreover, two sensorless control schemes are also proposed for the IPMSM drive system. One is the high-frequency signal injection method combined with the commutation-signal-based (CSB) speed estimation method, and the other is the high-frequency signal injection method combined with the model reference adaptive system (MRAS) speed estimation method. Both sensorless control schemes use high-frequency signal injection as the start-up strategy to achieve sinusoidal starting. When the motor speed gradually increases to a preset speed, the sensorless drive will switch to the CSB speed estimation method or the MRAS speed estimation method for high-speed control. The theories of the high-frequency signal injection method, the CSB and MRAS speed estimation methods and the PIDNN controller are introduced in detail. Furthermore, a DSP-based computer control system is developed to implement the sensorless DC inverter-fed compressor drive system and some experimental results are given to verify the feasibility of the proposed control schemes.

An Artificial-Intelligence Based Induction Motor Speed control and Estimation using conventional MRAS with dynamic reference modal

The Model Reference Adaptive System (MRAS) is probably the most widely applied speed sensor less drive control scheme. This paper proposes induction motor speed estimation using conventional MRAS and AIbased MRAS with stator resistance compensation. A conventional mathematical model based MRAS speed estimation scheme can give a relatively precise speed estimation result, but errors will occur during low frequency operation. Furthermore, it is also very sensitive to machine parameter variations. However, an AIbased MRAS system with a Stator Resistance Compensation model can improve the speed estimation accuracy and is relatively robust to parameter variations even at an extremely low frequency. These are verified by simulation results.

Improved MRAS‐type speed estimator for the sensorless induction motor drive

PurposeThis paper aims to obtain an accurate and robust estimation method of the rotor flux and speed for the sensorless induction motor (IM) drive.Design/methodology/approachThe reduced order observer has been used as an online tuned rotor flux model in the model reference adaptive system (MRAS) concept applied for the IM speed estimation. The output of this observer was used also as a feedback signal required in the direct field‐oriented control (DFOC) structure of the IM.FindingsIt is shown that a new rotor flux and speed estimator are more robust to motor parameter changes in comparison with the classical MRAS estimator and can work stably in the DFOC structure, in the wide speed range, even for relatively high (50 per cent) identification errors of equivalent circuit parameters of the IM.Research limitations/implicationsThe investigation looked mainly at the estimation accuracy performance and whole system stability while economic issues will still need to be addressed.Practical implicationsThe proposed new improved MRAS speed estimator can be easily realised using modern digital signal processors. The implementation was tested in an experimental set‐up with floating point DSP used as the system controller. The fixed‐point realisation needs to be developed to obtain the practical application in the industrial drive systems.Originality/valueThe application of the reduced order flux observer as a tuned flux model in the MRAS type speed estimator instead of the simple, but very sensitive to motor parameter uncertainties, current flux model, enables much better accuracy and stability of the rotor speed estimation in the complex DFOC structure than in the case of classical MRAS estimator.

Sensorless Vector Controlled Induction Machine in Field Weakening Region: Comparing MRAS and ANN-Based Speed Estimators

The accuracy of all the schemes that belong to vector controlled induction machine drives is strongly affected by parameter variations. The aim of this paper is to examine iron losses and magnetic saturation effect in sensorless vector control of induction machines. At first, an approach to induction machine modelling and vector control scheme, which account for both iron loss and saturation, is presented. Then, a model reference adaptive system (MRAS) based speed estimator is developed. The speed estimation is modified in such a way that iron losses and the variation in the saturation level are compensated. Thus by substituting an artificial neural network flux estimator into the MRAS speed estimator. Experimental results are presented to verify the effectiveness of the proposed approach.