State Estimation Of Induction Motor Research Articles

A study on improving the state estimation of induction motor

A study on improving the state estimation of induction motor

Design and Implementation of Hybrid Adaptive Extended Kalman Filter for State Estimation of Induction Motor

Today, induction motor (IM) is still the most popular electrical machine due to its robust and rare element-free structure, lower maintenance requirement, and cost-effective production. State estimation for this motor is the cornerstone for speed-sensorless control, fault-tolerant control, and fault diagnostics. Nonlinear Kalman filters, especially extended Kalman filters (EKFs), are the most preferred state and/or parameter estimation methods for IM. However, they require a stochastic system with complete process and measurement noise covariances for optimal estimations. These noise covariances, unknown or partially known in practice, vary under different operating conditions of the IM. To deal with this problem, various adaptive EKFs (AEKFs) have been proposed, which can compensate for the effect of varying noise covariances, but each approach has its own pitfalls. This article discusses the hybrid AEKF (HAEKF), which eliminates the problems of existing AEKFs. To demonstrate its effectiveness, the proposed HAEKF is compared qualitatively and quantitatively with existing AEKFs through simulation and experimental studies. Finally, improved estimation stability and performance are provided with the proposed HAEKF observer.

Robust state estimation using desensitized Divided Difference Filter

This paper develops a robust divided difference filtering approach based on the concept of Desensitized Kalman Filtering. The filters are formulated using a minimum variance cost function, augmented with a penalty function consisting of a weighted norm of the state sensitivities. Solutions are provided for first and second-order Divided Difference Filters. The resulting filters are non-minimum variance but exhibit reduced sensitivity to deviations in the assumed plant model parameters. The proposed algorithms are demonstrated using Monte Carlo simulation techniques for an induction motor state estimation problem with parameter uncertainties.

Sensorless induction machine observation with nonlinear magnetic characteristic

SUMMARYThis paper addresses the problem of state estimation in induction motors. Generally, the motor observer design has been dealt based on standard models neglecting the saturation effect in the magnetic characteristic. As a matter of fact, magnetic saturation cannot be ignored especially when considering control strategies (speed and torque) that involve large flux variations. Such large flux variations are necessary to meet optimal operation conditions in the presence of wide range load torque changes. On the other hand, it is well known that use of mechanical (speed and torque) sensors entails reliability issues. In this paper, a new sensorless adaptive observer is designed for induction machine based on a model that accounts for the nonlinear feature in the magnetic circuit. The observer consists of two interconnected state‐dependent gain observers and is formally shown to provide accurate estimates of the mechanical and magnetic variables using only stator current and voltage measurements. Copyright © 2013 John Wiley & Sons, Ltd.

Square Root Unscented Kalman Filters for State Estimation of Induction Motor Drives

This paper investigates the application, design, and implementation of the square root unscented Kalman filter (UKF) (SRUKF) for induction motor (IM) sensorless drives. The UKF uses nonlinear unscented transforms (UTs) in the prediction step in order to preserve the stochastic characteristics of a nonlinear system. The advantage of using the UT is its ability to capture the nonlinear behavior of the system, unlike the extended Kalman filter (EKF) that uses linearized models. The SRUKF implements the UKF using square root filtering to reduce computational errors. We discuss the theoretical aspects and implementation details of the SRUKF for IM drives. Experimental results for a direct-torque-controlled drive are presented for a wide speed range of operation, with focus on low-speed performance. A comparison with the conventional EKF and the UKF is included. Our results show that the SRUKF is a viable and powerful tool for IM state estimation.

High Gain Observer for Induction Motor in Presence of Magnetic Hysteresis

AbstractThe problem of state estimation in induction motors is considered in this paper. In most previous works, motor observers have been designed using standard models, neglecting the saturation and hysteretic effects in the machine magnetic circuit. As a matter of fact, these effects cannot be ignored especially when considering (speed, torque) control strategies involving wide range flux variations, necessary to meet optimal operation conditions when facing significant load torque changes. In this paper, a high gain observer is designed for induction motors based on a model that accounts for the nonlinear hysteretic nature of the magnetic circuit. The observer provides estimates of the magnetizing current and magnetic variables using available measurements i.e. stator currents and voltages, rotor speed. The observer is formally shown to enjoy nice convergence properties. The theoretical result is confirmed by simulation.

Sensorless Induction Machine Observer in Presence of Nonlinear Magnetic Characteristic

We are considering the problem of state estimation in induction motors. Generally, the motor observer design has been dealt based on standard models that neglect the saturation effect of the magnetic characteristic. As a matter of fact, magnetic saturation cannot be ignored especially when considering (speed, torque) control strategies that involve large flux variations. Such large flux variations are necessary to meet optimal operation conditions in presence of wide range load torque change. On the other hand, it is well known that the use of mechanical (speed, torque) sensors entails reliability issues. In this paper a new observer design is developed for induction machine based on a model that accounts for the nonlinear feature of the magnetic circuit. The observer provides estimates of the mechanical and magnetic variables using only stator currents and voltages measurements. The observer convergence is formally analyzed and illustrated by simulation.

Nonlinear Luenberger‐like observers for nonlinear MIMO systems

AbstractThis paper deals with the design of observers for a class of continuous time nonlinear multi‐input multi‐output systems with nonlinear outputs. Geometric tools are used to transform the original system into an appropriate observer canonical form. Furthermore, a pole placement technique is used to obtain a desired transient response of resulting error dynamics. The observer design is presented for two cases. In both cases, it is shown that the observer gain can be obtained from the solution of a Riccati equation. An illustrative example of state estimation in induction motors is presented to explain the proposed observer design. The performance of the method is also verified by numerical simulations. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

DISCRETE-TIME OBSERVER FOR INDUCTION MACHINE IN PRESENCE MAGNETIC SATURATION

We are considering the problem of state estimation in induction motors. One key feature of this work is that the problem is dealt with based upon a model that accounts for the saturation effect of the magnetic circuit characteristic. Indeed, magnetic saturation cannot be ignored, especially when high power machines are considered. The above model is first based upon to analyse the machine observability. Then, a continuous-time high-gain observer is presented and discretized for omplementation purpose. Time-discretization is necessary because real-time implementation can only be done using digital equipments. The discretization task constitutes a crucial issue due to the nonlinear feature of the (continuous) observer. It is coped with using the Taylor-Lie method and the (discrete-time) observer thus obtained is validated experimentally using an asynchronous motor of 7.5 KW.