Estimation Of Motor Parameters Research Articles

On-line Parameter Estimation of Interior Permanent Magnet Synchronous Motor using an Extended Kalman Filter

This paper presents estimation of d-axis and q-axis inductance of an interior permanent magnet synchronous motor (IPMSM) by using an extended Kalman filter (EKF). The EKF is widely used for control applications including the motor sensorless control and parameter estimation. The motor parameters can be changed by temperature and air-gap flux. In particular, the variation of the inductance affects torque characteristics like the maximum torque per ampere (MTPA) control. Therefore, by estimating the parameters, it is possible to improve the torque characteristics of the motor. The performance of the proposed estimator is verified by simulations and experimental results based on an 11kW PMSM drive system.

Real-Time Parameter Estimation of PMDC Motors Using Quantized Sensors

Establishing real-time models for electric motors is important when capturing authentic dynamic behavior of the motors to improve control performance, enhance robustness, and support diagnosis. Quantized sensors are less expensive, and remotely controlled motors mandate signal quantization. Such limitations on observations introduce challenging issues in motor parameter estimation. This paper develops estimators for model parameters of permanent-magnet direct current (PMDC) motors using quantized speed measurements. A typical linearized model structure of PMDC motors is used as a benchmark platform to demonstrate the technology and its key properties and benefits. Convergence properties are established. Simulations and experimental studies are performed to illustrate potential applications of the technology.

매입형 영구자석 동기전동기를 위한 새로운 전동기 상수 추정 방법

It is important to know exact values of Interior Permanent Magnet Synchronous Motors(IPMSM)` parameters such as stator resistance and inductance in order to have their high performance. This paper proposes a novel motor parameter(stator resistance, d&q axis inductance) estimation algorithm for IPMSM. The proposed estimation method utilizes back-EMF equations and model reference adaptive system(MRAS). The algorithm using back-EMF estimates d and q axis inductances in the constant torque region, and the stator resistance is estimated by using MRAS with the estimated inductance regardless of speed regions. The validity of the proposed algorithm is verified by simulations and experiments.

Accurate Induction Motor Estimator Based on Magnetic Field Analysis

Accurate induction motor parameter estimation is crucial for electrical drives. This paper presents a methodology enabling accurate estimation for induction motors response on pulse-width modulation inverter supply based on magnetic field analysis. Consideration of variations of magnetizing and iron loss parameters with loading in conjunction with harmonic iron loss associated to leakage inductances is essential for the estimator precision.

Closed-Loop Estimation of Permanent Magnet Synchronous Motor Parameters by PI Controller Gain Tuning

In this paper, we present a closed-loop estimation method of permanent magnet synchronous motor parameters by PI controller gain tuning. The idea of the proposed method is to tune the controller to cancel the pole of the motor transfer function with a controller zero and estimate motor parameters from the tuned controller gains. The induced EMF is cancelled by a feedforward compensation term. A systematic tuning procedure is illustrated based on sound theoretical derivations. Compared with the conventional methods using system identification or phasor diagrams, the proposed method does not require complex data analysis or test configuration. Only simple calculation and tuning are sufficient to estimate motor parameters while meeting the desired closed-loop performance. Simulation and experiment are presented to validate the proposed method

A review of RFO induction motor parameter estimation techniques

An induction motor is the most frequently used electric machine in high-performance drive applications. Control schemes of such drives require an exact knowledge of at least some of the induction motor parameters. Any mismatch between the parameter values used within the controller and actual parameter values in the motor leads to a deterioration in the drive performance. Numerous methods for induction machine on-line and off-line parameter estimation have been developed exclusively for application in high-performance drives. This paper aims at providing a review of the major techniques used for the induction motor parameter estimation. The paper is illustrated throughout with experimental and simulation examples related to various parameter estimation techniques.

A Review of RFO Induction Motor Parameter Estimation Techniques

An induction motor is the most frequently used electric machine in high-performance drive applications. Control schemes of such drives require an exact knowledge of at least some of the induction motor parameters. Any mismatch between the parameter values used within the controller and actual parameter values in the motor leads to a deterioration in the drive performance. Numerous methods for induction machine on-line and off-line parameter estimation have been developed exclusively for application in high-performance drives. This paper aims at providing a review of the major techniques used for the induction motor parameter estimation. The paper is illustrated throughout with experimental and simulation examples related to various parameter estimation techniques.

A dynamic decoupling control scheme for high-speed operation of induction motors

In a high-speed operation of a vector-controlled induction motor, coupling between d-q current dynamics impairs the characteristics of torque response. The feedforward decoupling scheme does not perform well if an error exists in the motor parameter estimation. We derive a dynamic decoupling condition when the two additional proportional integral current controllers are used. A great advantage of this dynamic decoupling controller is the robustness to the motor parameter estimation errors. Further, we observe that overmodulation methods lead to the violation of the decoupling condition, thereby yielding a poor performance in the high-speed high-power operation. As a method of resolving this problem, we propose a decoupling preserving overmodulation algorithm which also enhances the torque transient response. Through simulation and experimental results, we demonstrate the improved performance of the proposed controller.

Monitoring the thermal condition of permanent-magnet synchronous motors

This paper describes a novel approach to monitoring the condition of small permanent-magnet synchronous motors (PMSM) operating under thermal stress. The approach begins with the estimation of temperature-dependent motor parameters from measurements of line voltages and currents. The parameters are then used to derive estimates of motor temperatures. Next, the electrically estimated temperatures are combined with a surface measurement of motor temperature and a dynamic thermal model of the motor to yield an observer that is a Kalman filter. The temperatures estimated by the observer are used for failure prevention. Finally, by modifying the observer, it is tuned to use the geometric properties of its innovation for failure detection. The innovation, that is, the difference between the thermally and electrically estimated temperatures, is monitored and compared against appropriate thresholds to detect failures. Failure detection is demonstrated experimentally, and shown to be capable of distinguishing the conditions of normal operation, and operation with obstructed cooling.