Measured Stator Currents Research Articles

A blind estimation of speed and rotor position of permanent magnet synchronous motor from the measurements of stator currents using extended Kalman filter

A number of techniques have been developed for estimation of speed or rotor position in motor drives. In this paper, an extended Kalman filter (EKF) is developed for the estimation of speed and rotor position using only the measurements of three phase stator currents. Conventional mechanical sensors are not needed for this estimation. The paper proposes the modelling of the dynamic equations of the permanent magnet synchronous motor (PMSM) to describe the dynamics in terms of direct axis current and voltage, quadarture axis current and voltage, rotor position and synchronous speed. Unlike the earlier papers in this direction, the present work does not take any input like vd or vq into the Kalman filter, but uses only the sensed line currents as measurements. The performance evaluation of the extended Kalman filter with online estimation of the speed and rotor position of a PMSM is presented with simulation results, for a varying load torque.

Forced Dynamics Control of the Elastic Joint Drive with Single Rotor Position Sensor

Position control system of moderate precision based on ‘forced dynamics control’ for the drive with significant vibration modes is described. To exploit the only position sensor on the motor side, all necessary control variables are estimated in observers based on motor position and stator current measurements. The designed controller is of a cascade structure, comprising an inner speed control loop, which respects vector control principles and an outer position control loop, which is designed to control load angle with prescribed dynamics in the presence of flexible modes. Simulations of the overall control system indicate that the proposed control system exhibits the desired robustness and therefore warrants further development and experimental investigation.

Stator Flux Observer for Induction Motor Based on Tracking Differentiator

Voltage model is commonly used in direct torque control (DTC) for flux observing of asynchronous motor. In order to improve low-speed and dynamic performance of the voltage model, a modified low-pass filter (LPF) algorithm is proposed. Firstly, the tracking differentiator is brought in to modulate the measured stator current, which suppresses the measurement noise, and then amplitude and phase compensation is made towards the stator electromotive force (EMF), after which the stator flux is obtained through a low-pass filter. This method can eliminate the dynamic error of flux filtered by LPF and improve low-speed performance. Experimental results demonstrate effectiveness and improved dynamic performance of such method.

Adaptive Output Feedback Voltage-Based Control of Magnetically-Saturated Induction Motors

The problem of controlling the induction motor -model with magnetic saturation is considered. An adaptive controller is developed under the assumption of measurable stator currents and speed only with unknown rotor resistance and load torque. All the unknown parameters are assumed constant or slowly varying and are estimated online by the controller. Simulation results are provided for illustration.

ADALINE approach for induction motor mechanical parameters identification

Two new methods to identify the mechanical parameters in induction motor based field oriented drives are presented in this paper. The identified parameters are: the moment of inertia and the viscous damping coefficient. The proposed methods are based on the adaptive linear neuron (ADALINE) networks. The two parameters are derived and optimized during the online training process. During the identification phase, the motor torque is controlled by the well-known field oriented control strategy. This torque is subjected to variations in order to obtain mechanical speed transients. The two proposed methods are simple to implement compared to the previous techniques. They require only the stator current and mechanical speed measurements. Finally, the effectiveness of the two methods and the accuracy of the derived parameters are proven experimentally by two direct starting tests. The originality of this work is the building of a model representation that it is suitable for implementation with ADALINE networks. This leads to a simple implementation and ease of mechanical parameters identification.

Nonlinear speed tracking control for sensorless PMSMs with unknown load torque: From theory to practice

In this paper the speed tracking control problem for sensorless (nonsalient-pole surface) permanent magnet synchronous motors (PMSMs) with unknown load torque is addressed. Simulation and experimental results are presented for the first nonlinear adaptive control which (i) relies on a closed loop stability proof using nonlinear stability tools; (ii) feeds back stator current and voltage measurements only without using non-robust open loop integration of motor dynamics. Satisfactory performances are obtained in practice in conditions which can be inferred by deeply analyzing the reported theoretical analysis.

Wind Speed and Rotor Position Sensorless Control for Direct-Drive PMG Wind Turbines

This paper proposes a wind speed and rotor position sensorless control for wind turbines directly driving permanent magnetic generators (PMGs). A sliding-mode observer is designed to estimate the rotor position of the PMG by using the measured stator currents and the commanded stator voltages obtained from the control scheme of the machine-side converter of the PMG wind turbine. The rotor speed of the PMG (i.e., the turbine shaft speed) is estimated from its back electromotive force using a model adaptive reference system observer. Based on the measured output electrical power and estimated rotor speed of the PMG, the mechanical power of the turbine is estimated by taking into account the power losses of the wind turbine generator system. A back-propagation artificial neural network is then designed to estimate the wind speed in real time by using the estimated turbine shaft speed and mechanical power. The estimated wind speed is used to determine the optimal shaft speed reference for the PMG control system. Finally, a sensorless control is developed for the PMG wind turbines to continuously generate the maximum electrical power without using any wind speed or rotor position sensors. The validity of the proposed estimation and control algorithms are shown by simulation studies on a 3-kW PMG wind turbine and are further demonstrated by experimental results on a 300-W practical PMG wind turbine.

EKF modeling of field oriented control of six-phase induction motor

This paper deals with using Extended Kalman Filter (EKF) as a powerful tool in estimate of parameters and states of six-phase induction motor (6PIM). The speed-sensorless field-oriented control of 6PIM supplying by SVPWM is used to estimate of parameters in parameter variations. The EKF algorithm estimate stator flux, stator current, and stator resistance by measurement of stator currents of inverter. The estimation algorithms based on the stator fluxes is tested under load variations in the high velocity. The simulation results presented in this paper explain the performance of proposed method in the six-phase induction machine drive.

An integrated electro-mechanical model of motor-gear units—Applications to tooth fault detection by electric measurements

Fault diagnosis in geared transmissions is traditionally based on vibration monitoring but, in a number of cases, sensor implementation and signal transfer from rotary to stationary parts can cause problems. This paper presents an original integrated electro-mechanical model aimed at testing the possibility and the interest of tooth fault detection based on electric measurements on the motor stator. The motor is simulated using Kron's transformation while the mechanical transmission is accounted for by a lumped parameter model. Tooth defects are assimilated to distributions of initial separations between the mating flanks whose positions and shapes are controlled. A unique non-linear parametrically excited differential system is obtained, which provides direct access to both the electrical and mechanical variables. A number of results are presented, which illustrate the possibility of tooth fault detection by stator current measurements with regard to the position and dimensions of the defect.

INDIRECT FIELD-ORIENTED CONTROL OF FIVE-PHASE INDUCTION MOTOR DRIVES

Five -phase ac motor drives are nowadays considered for various applications, due to numerous advantages that they offer when compared to three-phase motors. Variable speed induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. To replace the sensor, information of the rotor speed is extracted from measured stator currents and voltages at motor terminals. This paper proposes speed estimation method using model reference adaptive system (MRAS) to improve the performance of a sensorless vector controller of five-phase induction motor (IM). In the proposed method, the stator current is used as the state variable to estimate the speed. Since the stator current error is represented as a function of the first degree for the error value in the speed estimation, the proposed method can provide fast speed estimation and is also, more robust to variations in the parameters, compared with other MRAS methods. Consequently, this method can improve the performance of a sensorless vector controller in a low speed region and at zero-speed. The proposed method is verified by simulation using the Matlab/Simulink package. The performance of the proposed system is investigated at different operating conditions. The proposed controller is robust and suitable for high performance five-phase induction motor drives. Simulation results validate the proposed approaches.

Modified Euler integration based control of a five-phase induction motor drive

In principle, control methods for multi-phase machines are the same as for three-phase machines. Variable speed induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. To replace the speed sensor, information of the rotor speed is extracted from measured stator currents and voltages at motor terminals. Vector-controlled drives require estimating the magnitude and spatial orientation of the fundamental magnetic flux in the stator or in the rotor. Open-loop estimators or closed-loop observers are used for this purpose. They differ with respect to accuracy, robustness, and sensitivity against model parameter variations. This paper analyses operation of a modified Euler integrationbased sensorless control of vector controlled five-phase induction machine with current control in the stationary reference frame. A linear neural network has been then designed and trained online by means of back propagation network (BPN) algorithm, differently from that in the literature which employs a nonlinear back propagation network (BPN) algorithm. The Artificial Neural Network (ANN)-Model Reference Adaptive System (MRAS) based sensorless operation of a three-phase induction machine is well established and the same principle is extended in this paper for a five-phase induction machine. Performance, obtainable with hysteresis current control, is illustrated for a number of operating conditions on the basis of simulation results. Full decoupling of rotor flux control and torque control is realised. Dynamics, achievable with a five-phase vector controlled induction machine, are shown to be essentially identical to those obtainable with a three-phase induction machine. International Journal of Engineering, Science and Technology, Vol. 2, No. 6, 2010, pp. 218-230

Novel Approach in Sensorless Speed Control of Salient Axial-Gap Self-Bearing Motor Using Extended Electromotive Force

Axial-gap self-bearing motor (AGBM) is an electrical combination of an axial flux motor and a thrust magnetic bearing, hence it can support rotation and magnetic levitation without any additional windings. The goal of this paper is utilization of the state observer to research a new capability of sensorless speed control of a salient AGBM. First, analytical and theoretical evaluation for a sensorless speed vector control of a salient AGBM is presented. The approach is based on the estimation of the extended electromotive force (EEMF) through a Luenberger Observer (LO) with help of reference stator voltages, measured stator currents and measured axial displacement. Then, experiment is implemented based on dSpace1104 with two three-phase inverters. The experimental results confirm that the AGBM can simultaneously produce levitation force and rotational torque. Moreover, speed and axial displacement can be independently controlled without speed sensor.

RECURSIVE LEAST SQUARE ALGORITHM FOR ESTIMATING PARAMETERS OF AN INDUCTION MOTOR

This paper presents a linear parameter estimation technique used toestimate the rotor resistance, self inductance of the rotor winding,stator resistance and the stator leakage inductance of an inductionmotor. Such estimation is important for achieving high estimationperformance of induction motor drives. The parameters estimationmodel expresses the relationships of the dynamic machine model interms of measurable stator voltages, currents and motor speed. Thismodel is represented by a linear regression equation from whichmachine parameters can be obtained using a recursive least squares(RLS) estimation algorithm. Simulation results are presented tovalidate the proposed estimation algorithm with reasonable accuracy ofthe estimated parameters regardless of load conditions. Comparisonsbetween experimental and calculated steady-state performances usingthe estimated parameters are also presented.

Parameter estimation of an induction machine using advanced particle swarm optimisation algorithms

This study proposes a new application of two advanced particle swarm optimisation (PSO) algorithms for parameter estimation of an induction machine (IM). The inertia weight, cognitive and social parameters and two independent random sequences are the main parameters of the standard PSO algorithm which affect the search characteristics, convergence capability and solution quality in a particular application. Two advanced PSO algorithms, known as the dynamic particle swarm optimisation (dynamic PSO) and chaos PSO algorithms modify those parameters to improve the performance of the standard PSO algorithm. The algorithms use the measurements of the three-phase stator currents, voltages and the speed of the IM as the inputs to the parameter estimator. The experimental results obtained compare the estimated parameters with the IM parameters achieved using traditional tests such as the dc, no-load and locked-rotor tests. There is also a comparison of the solution quality between a genetic algorithm (GA), standard PSO, dynamic PSO and chaos PSO algorithms. The results show that the dynamic PSO and chaos PSO algorithms are better than the standard PSO algorithm and GA for parameter estimation of the IM.

High performance sensorless speed vector control of SPIM Drives with on-line stator resistance estimation

The aim of this paper is to implement a sensorless Indirect Stator Field Oriented Control (ISFOC) of Single-Phase Induction Motor Drive (SPIMD) with stator resistance tuning. The proposed method for the estimation of speed is based only on measurement of stator currents. A very simple identification algorithm using d-axis stator current error for identifying the stator resistance is proposed. Experimental results for SPIMD are presented and analyzed by using a dSpace system with DS1104 control board based on the Digital Signal Processor (DSP) TMS320F240. Simulated results are compared with experimental results on a test 1.1 kW SPIM setup. The agreement between simulated and experimental results proves the validity of the proposed method.

A robust sensorless output feedback controller of the induction motor drives: new design and experimental validation

In this article, a sensorless output feedback controller is designed in order to drive the induction motor (IM) without the use of flux and speed sensors. First, an observer that uses only the measured stator currents is synthesised to estimate the mechanical variables (speed and load torque) and the magnetic variables (fluxes) by structurally taking into account the unobservability phenomena of the sensorless IM (SIM) and the parametric uncertainties. Second, a current-based field-oriented sliding mode control that uses the flux and the speed estimates given by the former observer is developed so as to steer the estimated speed and flux magnitude to the desired references. Since the observer error dynamic is independent from the known input control and depends on the IM parametric uncertainties, a kind of separation principle is introduced to guarantee the practical stability of the whole closed-loop system ‘observer–controller’ (‘O-C’) according to observability and unobservability time variation. A significant benchmark taking into account the unobservability phenomena of the SIM is presented to show the performances of the whole control scheme against experimental set-up.

On Sensorless Induction Motor Drives: Sliding-Mode Observer and Output Feedback Controller

In this paper, a sensorless output feedback controller is designed in order to drive the induction motor (IM) without the use of flux and speed sensors. First, a new sliding-mode observer that uses only the measured stator currents is synthesized to estimate the speed, flux, and load torque. Second, a current-based field-oriented sliding-mode control is developed so as to steer the estimated speed and flux magnitude to the desired references. A stability analysis based on the Lyapunov theory is also presented in order to guarantee the closed-loop stability of the proposed observer-control system. Two experimental results for a 1.5-kW IM are presented and analyzed by taking into account the unobservability phenomena of the sensorless IM.

Hilbert versus Concordia transform for three-phase machine stator current time-frequency monitoring

This paper deals with mechanical fault diagnosis in three-phase induction machines from stator current measurements. According to machine models, mechanical faults lead to amplitude and/or phase modulations of the measured stator current with possibly time varying carrier frequency. The modulation diagnosis requires a univocal definition of the instantaneous phase and amplitude. This is performed by associating a complex signal to the real measured one. For a convenient separate modulation diagnosis, the complex signal instantaneous phase and amplitude are expected to carry, respectively, information about the phase and amplitude modulations. The complex signal is classically obtained through the Hilbert transform. Under Bedrosian conditions, the so-called analytic signal allows a separate modulation diagnosis. However, mechanical faults may also produce fast modulations violating the Bedrosian conditions. This study proposes an alternative complex signal representation which takes advantage of the three stator current measurements available in a three-phase machine. From two stator current measurements, the Concordia transform builds a complex vector, the so-called space vector, which unconditionally allows separate modulation diagnosis. This paper applies and compares the Hilbert and Concordia transforms, theoretically and in case of simulated and experimental signals with various modulation frequency ranges.

Robust Angle-Sensorless Control of a PMSM Bearingless Pump

In the semiconductor industry, where bearingless pump systems are employed as the state-of-the-art technology, the trend goes toward higher fluid temperatures (150degC and more) in order to further increase process efficiency. This fact translates into the requirement of a high-temperature bearingless pump system and/or the elimination of thermal-critical components such as Hall sensors. This paper introduces a new method for a hall-sensorless control of a permanent-magnet synchronous machine bearingless pump in its operating range from 0 to 8000 r/min and from zero load to full load. The sensorless operation is performed by the following three novel control functionalities: a controlled startup routine, enabling a sure levitation and zero-angle setting; an open-loop angle estimation based on stator voltage and stator current measurement and known machine parameters; and an angle synchronization establishing a robust operation of the pump in the whole operating range even for a large machine parameter drift. In particular, considering the temperature degrading of the permanent-magnet flux density, the novel robust control concept is of great benefit for bearingless pump systems employed in high-temperature applications.

Real-Time Speed and Flux Adaptive Control of Induction Motors Using Unknown Time-Varying Rotor Resistance and Load Torque

In this paper, an algorithm for direct speed and flux adaptive control of induction motors using unknown time-varying rotor resistance and load torque is described and validated with experimental results. This method is based on the variable structure theories and is potentially useful for adjusting online the induction motor controller unknown parameters (load torque and rotor resistance). The presented nonlinear compensator provides voltage inputs on the basis of rotor speed and stator current measurements, and generates estimates for both the unknown parameters and the nonmeasurable state variables (rotor flux and derivatives of the stator current and voltage) that converge to the corresponding true values. Experiments show that the proposed method achieved very good tracking performance within a wide range of the operation of the induction motor with online variation of the rotor resistance: up to (87%). This high tracking performance of the rotor resistance variation demonstrates that the proposed adaptive control is beneficial for motor efficiency. The proposed algorithm also presented high decoupling performance and very interesting robustness properties with respect to the variation of the stator resistance (up to 100%), measurement noise, modeling errors, discretization effects, and parameter uncertainties (e.g., inaccuracies on motor inductance values). The other interesting feature of the proposed method is that it is simple and easily implementable in real time. Comparative results have shown that the proposed adaptive control decouples speed and flux tracking while standard field-oriented control does not.