Speed Sensorless Control Of Induction Motor Research Articles

Current Sensor Fault-Tolerant Control Strategy for Speed-Sensorless Control of Induction Motors Based on Sequential Probability Ratio Test

In the speed-sensorless vector control of induction motors (IMs), the speed estimation accuracy suffers from the deteriorated current measurement caused by the current sensor faults, such as open circuit in one phase, DC bias, and odd harmonics. In this paper, a novel speed estimation strategy based on the current sensor fault-tolerant control is proposed to improve the speed estimation accuracy under the current sensor faults. First, to detect the current sensor faults in real time, the sequential probability ratio test is introduced to the system by using the innovations of the extended Kalman filter (EKF). Second, to ensure speed estimation accuracy, a double-cascading second-order generalized integrator (DSOGI) is employed to reconstruct the faulty current information when a fault is identified. Finally, the reconstructed current information is fed back to the sequential probability extended Kalman filter (SPEKF), which estimates the rotor speed of the IM, and high-accuracy speed estimation under the condition of current sensor faults is achieved. The effectiveness of the proposed strategy is validated by a series of experiments, which were conducted on a 3 kW induction motor drive platform.

Speed-Sensorless Control of Induction Motor Drives With a STA-FLL Speed Estimation Scheme

Speed-sensorless control (SSC) techniques enhance the overall reliability while reducing the cost, and thus become attractive in induction motors (IMs). Simplicity and flexibility are the main features that make phase-locked loop (PLL)- and frequency-locked loop (FLL)-based speed estimation schemes stand out among considerable estimation methods. However, many PLL and FLL schemes exhibit a poor estimation accuracy in the cases of acceleration and deceleration (AaD). Accordingly, an adaptive second-order generalized integrator-FLL (SOGI-FLL)-based scheme is introduced. While, the issue of the estimated speed feedback (ESF) in the adaptive SOGI-FLL scheme remains an obstacle. With the above, a speed estimation scheme that combines the super-twisting algorithm with an FLL (STA-FLL) is proposed for the SSC of IM drives in this paper. In the proposed STA-FLL scheme, an STA-based quadrature signal generator (STA-QSG) is properly designed, in which the ESF is cancelled to further ensure speed estimation. Moreover, a closed-loop flux observer is adopted in the implementation to enhance the proposed STA-FLL scheme in terms of disturbance mitigation. Experimental tests are performed to validate the proposed STA-FLL scheme.

An Adaptive Active Disturbance Rejection Control Strategy for Speed-Sensorless Induction Motor Drives

Speed-sensorless control of induction motor drives based on the extended state observer (ESO) attracts much popularity due to its key characteristics, e.g., satisfactory estimation performance, and high robustness against disturbances. However, the conventional ESO generally uses fixed high gains to achieve fast convergence. This may bring about the concern of noise sensitivity. Moreover, a proportional-integral (PI)-type speed controller is mostly adopted in speed-sensorless control of induction motor drives, which may cause unsatisfactory system dynamics. To address these, an adaptive active disturbance rejection control (ADRC) strategy is proposed for speed-sensorless induction motor drives in this article. In this scheme, a third-order adaptive ESO (AESO) is first used to estimate the speed, the phase, and the overall disturbance. Then, a speed controller based on the state error feedback control rate (SEFCR) is designed to improve system dynamics. In practice, disturbances like dc offsets may further challenge estimation performance, and hence, a closed-loop flux observer (CLFO) is employed to deal with this issue. Additionally, a parameter sensitivity analysis of the CLFO is also provided to evaluate the performance of the proposed scheme. Finally, extensive experimental tests have been carried out to validate the effectiveness of the proposed.

A Single-Stage SPV-Fed Reduced Switching Inverter-Based Sensorless Speed Control of IM for Water Pumping Applications

This article elaborates on a reduced switch count-based inverter for a single-stage solar photovoltaic (SPV) fed induction motor (IM) with sensorless speed control for water pumping applications. The traditional SPV-fed IM for water pumping applications requires a six-switch voltage source inverter (SSVSI) for transforming the DC power from the SPV system into AC power. However, the same performance is achieved using a four-switch voltage source inverter (FSVSI). Here, the entire system requires less number of switches and hence reduces switching losses and cost as compared to the traditional solar water pumping system. Moreover, the sensorless speed control is implemented using a speed estimator to reduce the overall cost further and enhance reliability. The reference voltage ( V d c r ) is achieved using an adapted incremental conductance (AINC), and the control of IM is performed using direct vector control (DVC). The control signals for the proposed system are generated using DSPACE DS-1104 for real-time implementation. The proposed SPV-fed FSVSI-based 1-HP IM operation is performed at different irradiation levels in the MATLAB-Simulink environment and validated experimentally.

PLL- and FLL-Based Speed Estimation Schemes for Speed-Sensorless Control of Induction Motor Drives: Review and New Attempts

Phase-locked loops (PLLs) and frequency-locked loops (FLLs) are of importance in power and energy applications. Both technologies have been introduced to speed-sensorless- controlled motor drives, and increasing applications of PLLs and FLLs for speed estimation are foreseen. To enable a proper and good design, a thorough review of the PLL- and FLL-based speed estimation schemes is then provided in this article. It is revealed through the review that many PLL- and FLL-based estimation schemes fail to accurately track a frequency ramp (i.e., obvious estimation errors appear), which may lead to a compromised estimation accuracy when these schemes that are applied in induction motor drives operating during acceleration and deceleration processes. To address this, the proven speed estimation schemes together with new attempts are also presented in this article. Moreover, various challenges to the PLL- and FLL-based speed estimation schemes, including harmonics, dc offsets, and parameter variations, are considered when evaluating these schemes. Solutions to tackle these disturbances are accordingly presented. In addition, two representative estimation schemes are exemplified through experimental tests. Finally, further challenges in using the PLL- and FLL-based schemes for speed estimation are discussed.

A Delay-Based Frequency Estimation Scheme for Speed-Sensorless Control of Induction Motors

The use of costly speed sensors challenges the overall system reliability in induction motor drives. Hence, speed-sensorless control techniques are desired in high-performance induction motors, where estimating the motor speed is of importance. Distinctive advantages, e.g., structural and control simplicity and acceptable performance, have pushed the synchronization technique (ST)-based estimation schemes popular for motor drives. However, in many ST-based schemes, their performance may be degraded during acceleration and deceleration. To address this, a delay-based frequency estimation (DFE) scheme is explored for speed-sensorless control of induction motors in this article. More specifically, speed estimation in the proposed DFE scheme is achieved by using the estimated rotor flux and its delay signals. Additionally, disturbances like dc offsets are also considered, and thus a closed-loop flux observer is implemented in the proposed DFE scheme to maintain speed estimation. The performance of the proposed DFE scheme is investigated by experimental tests.

Speed-sensorless control of induction motors based on adaptive EKF

Speed-sensorless control of induction motors based on adaptive EKF

Full-Order Terminal Sliding-Mode-Based Sensorless Control of Induction Motor With Gain Adaptation

This article proposes a sensorless speed control approach of Induction motor (IM) applying full-order terminal sliding-mode (FOTSM) control theory. The speed control system consists of three feedback control loops, i.e., the speed-, flux-, and current-loops. The related controllers in these three feedback loops are designed using FOTSM to enhance the robustness and dynamic performance, eliminate the singularity, and attenuate the chattering. The virtual control technique is utilized in the outer-loop speed controller to compensate unmatched uncertainties in the system, such as load disturbance and some parameter variations. The integral-type continuous control law with gain adaptation algorithm guarantees that the current references are smooth. In the inner-loop controllers, the actual voltage control signals can force the tracking errors of the currents to converge to its equilibrium point within finite time. Meanwhile, the FOTSM observer is designed for estimating the flux and speed of the motor simultaneously. Finally, the experiment results have demonstrated the effectiveness and feasibility of the proposed sliding-mode controllers and observers for the sensorless speed control of IM.

Speed-Sensorless Control of Induction Motors With an Open-Loop Synchronization Method

Speed estimation schemes based on the closed-loop synchronization (CLS) methods for speed-sensorless control of motor drives attract much popularity due to several advantages, for example, easy implementation, high flexibility, and acceptable performance. However, most of the existing CLS-based estimation schemes may suffer from performance degradation during frequency ramps. Considering this, an attempt of the type-3 phase-locked loop (PLL)-based scheme is made. This solution, however, may adversely affect the system dynamics and stability margin. To address these issues, an open-loop synchronization (OLS) method is proposed for speed-sensorless control of induction motor drives in this article. In the proposed scheme, the estimated speed is obtained according to the sinusoidal signals and their time-delay signals, rather than increasing the system order. With this, system dynamics and stability margin are maintained. In practice, the disturbance of dc offsets is of concern in induction motor drives. Thus, a closed-loop flux observer is adopted to guarantee the estimation performance under dc offsets. The performance of the proposed OLS scheme is investigated and compared with that of the CLS schemes and the type-3 PLL scheme through experimental tests.

Speed-Sensorless Control of Linear Induction Motor Based on the SSLKF-PLL Speed Estimation Scheme

In most motor drive applications, the speed sensor is of concern because it potentially deteriorates the system reliability and increases the system cost. To mitigate the adverse effects of speed sensors, various speed estimation schemes are employed to achieve speed-sensorless control of motor drives. With this, the concept of the steady-state linear Kalman filter-based phase-locked loop (SSLKF-PLL) is adopted in this article to enable speed estimation for speed-sensorless control of linear induction motor drives. However, the SSLKF-PLL scheme may be challenged by disturbances, e.g., harmonics and parameter variations in practice. To address this issue, the disturbance rejection schemes, including the prefilters and amplitude normalization unit, are developed for the SSLKF-PLL scheme in this article. Additionally, the dynamic performance analysis and parameter tuning of the SSLKF-PLL scheme are detailed by using the derived small-signal model. Experimental tests have validated the analysis and discussions of the SSLKF-PLL scheme.

Integral Sliding Mode Control for Starting Speed Sensorless Controlled Induction Motor in the Rotating Condition

Starting in the rotating condition is an important technology for induction motor (IM) speed sensorless control. The key of this technology is that the initial speed of the IM in the rotating condition should be estimated quickly and accurately. In this article, an integral sliding mode control method based on the nonlinear model of the rotor back electromotive force is designed to start a speed sensorless controlled IM in the rotating condition. The stability is guaranteed by Lyapunov stability analysis, and the robustness to rotor time constant and disturbance are analyzed. Meanwhile, in comparison with the proposed control method, the existing input–output feedback linearization (IOFL) control method is introduced and the stability and robustness are also analyzed. Compared with the existing IOFL control method, the proposed control method can estimate the initial speed in the rotating condition without overshoot and has better dynamic performance and robustness. In addition, the proposed control method can be applied not only to the restart operation but also to the normal operation. Only a single control strategy is required from the restart operation to the normal operation. The proposed method is verified by simulation and experiments using a 7.5 kW IM.

ASENKRON MOTORUN İNDİRGENMİŞ-DERECELİ UYARLAMALI GENİŞLETİLMİŞ KALMAN FİLTRESİ İLE DURUM VE PARAMETRE KESTİRİMİ

In this study, the design of reduced-order adaptive extended Kalman filter (EKF) for speed-sensorless control of induction motors (IMs) is performed, and its performance is tested using it in a speed-sensorless direct vector controlled drive system under simulations. The proposed observer estimates the stator stationary axis components of rotor fluxes and rotor mechanical speed required for vector control in addition to disturbance load torque. On the other hand, estimation performance of EKFs depends on the correct selection of system ( ) and measurement ( ) error covariance matrices. In the literature, these matrices are generally assumed as constant and determined by the trial-and-error method. However, those matrices are affected by operating conditions of IM and should be updated according to operating conditions in order to obtain higher performance estimations. Since the simultaneously update of both and may lead to divergence or tracking problems, only is updated considering operating conditions by adaptive EKF (AEKF) algorithm having a forgetting factor. In addition, AEKF has been designed as reduced-order with the aim of reduction of its computational burden for real-time applications.

Nonlinear Effects of Three-Level Neutral-Point Clamped Inverter on Speed Sensorless Control of Induction Motor

In the model reference adaptive speed observer, the induction motor supply voltage is used as the input of the reference model. However, measuring the supply voltage complicates the system and increases the cost, so the command voltage calculated by the controller is generally used instead of the actual supply voltage in the drive system. However, due to the nonlinear effects of the inverter, the voltage calculated by the controller is different from the actual supply voltage, resulting in a speed observation deviation. This paper analyzes the multiple effects that cause the three-level neutral-point clamped (TL-NPC) inverter output voltage and command voltage deviation. A voltage deviation compensation measure based on the volt-second balance principle is proposed. In this context, the expression of the rotational speed deviation caused by the voltage deviation is derived rigorously and in detail. Finally, the effectiveness of the voltage compensation measure is verified by experiments. The experimental results are basically consistent with the theoretical derivation expressions. The method and analysis in this paper is applicable to induction motor speed sensorless control systems driven by two-level and other multilevel inverters.

Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors

Abstract This paper aims to introduce a novel extended Kalman filter (EKF) based estimator including observability analysis to the literature associated with the high performance speed-sensorless control of induction motors (IMs). The proposed estimator simultaneously performs the estimations of stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including the viscous friction term, and reciprocal of total inertia by using measured stator phase currents and voltages. The inertia estimation is done since it varies with the load coupled to the shaft and affects the performance of speed estimation especially when the rotor speed changes. In this context, the estimations of all mechanical state and parameters besides flux estimation required for high performance control methods are performed together. The performance of the proposed estimator is tested by simulation and real-time experiments under challenging variations in load torque and velocity references; and in both transient and steady states, the quite satisfactory estimation performance is achieved.

Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control

This paper presents a novel hybrid estimator consisting of an extended Kalman filter (EKF) and an active power-based model reference adaptive system (AP-MRAS) in order to solve simultaneous estimation problems of the variations in stator resistance ([Formula: see text]) and rotor resistance ([Formula: see text]) for speed-sensorless induction motor control. The EKF simultaneously estimates the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator currents, the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator fluxes, rotor angular velocity ([Formula: see text]), load torque ([Formula: see text]) and [Formula: see text], while the AP-MRAS provides the online [Formula: see text] estimation to the EKF. Both the AP-MRAS, whose adaptation mechanism is developed with the help of the least mean squares method in this paper, and the EKF only utilize the measured stator voltages and currents. Performances of the proposed hybrid estimator in this paper are tested by challenging scenarios generated in simulations and real-time experiments. The obtained results demonstrate the effectiveness of the introduced hybrid estimator, together with a [Formula: see text] reduction in the processing time and size of the estimation algorithm in terms of previous studies performing the same estimations of the states and parameters. From this point of view, it is the first such study in the literature, to our knowledge.

The Comparisons of Optimized Extended Kalman Filters for Speed-Sensorless Control of Induction Motors

This paper presents the comparisons of optimized extended Kalman filters (EKFs) using different fitness functions for speed-sensorless vector control of induction motors (IMs). In order to achieve high performance estimations of states/parameter by EKF algorithm, state and noise covariance matrices must be accurately selected. For this aim, instead of using time-consuming trial-and-error method to determine those covariance matrices, in this paper EKF algorithm is optimized by differential evolution algorithm (DEA) and multi-objective DEA (MODEA) with the utilization of different fitness functions. The optimally obtained set of each covariance matrices is used in EKF algorithm built on the same IM model and thus, the estimation results of the optimized EKF algorithms are compared in real-time experiments in order to conclude which fitness function is better for motion control applications.

Novel version of bi input-extended Kalman filter for speed-sensorless control of induction motors with estimations of rotor and stator resistances, load torque, and inertia

Novel version of bi input-extended Kalman filter for speed-sensorless control of induction motors with estimations of rotor and stator resistances, load torque, and inertia

Efficiency Optimization in Speed-Sensorless Control of Induction Motors

An efficiency optimization model for induction motors with speed-sensorless control is presented in this paper. An mathematical loss model with stator iron loss in DTC(Direct Torque Control) system is used to calculate the motors loss, the loss efficiency and the optimal flux. Additionally, the efficiency optimization control strategy combined with the speed-sensorless model is used to rebuild the simulation modeling. The simulation results with the proposed control strategy show superior effects compared to the traditional control methods. The optimal control strategy can be achieved to improve the motor efficiency.

Control of Four Switch Three Phase Inverter Fed Induction Motor Drives Based Speed and Stator Resistance Estimation

This paper presents sensorless position and speed control for a four-switch three-phase inverter (FSTPI) fed induction motor drive. Accurate knowledge of stator resistance is of utmost importance for correct operation of a number of speed sensorless induction motor control schemes in the low speed region. Since stator resistance inevitably varies with operating conditions, stable and accurate operation at near-zero speed requires an appropriate identification algorithm for the stator resistance. The paper proposes such an identification algorithm, which is developed for the rotor flux based model reference adaptive system (MRAS) type of the speed estimator in conjunction with a rotor flux oriented control scheme. In this speed estimation method only one (out of the two available) degree of freedom is utilized for speed estimation. It is utilize the second available degree of freedom as a mean for adapting the stator resistance. The parallel stator resistance and rotor speed identification algorithm is developed in a systematic manner, using Popov’s hyper stability theory. It increases the complexity of the overall control system insignificantly and enables correct speed estimation and stable drive operation at near-zero speeds. The proposed speed and position estimator with parallel stator resistance identification for FSTPI fed induction motor at very low speed under high load operation is verified by simulation and experimental results. The results show the robustness of the proposed method with FSTPI. DOI: http://dx.doi.org/10.11591/ijpeds.v4i2.5057

Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive

n speed sensorless controlled induction motor drive, the rotor flux based Model Reference Adaptive System (MRAS) for is more popular. In MRAS, the voltage model equations are used in the reference model, which leads to poor performance of the drive at low speeds. In this paper, a new stator current based MRAS is presented. In this measured stator currents of induction motor are used as a reference model to avoid the use of a pure integrator. Estimated stator currents are used as an adjustable model. The estimation of stator currents requires the information of the rotor flux which can be obtained from the voltage or current model. This encounters instability and dc drift. To overcome these problems, flux observer is proposed based on Neural Network (NN). This offline trained multilayer feed-forward NN flux observer applied to indirect vector controlled speed sensorless induction motor drive has been verified by MATLAB/SIMULINK.