Machine Model Based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: a Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space

Machine model based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: A Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space

FPGAs in Industrial Control Applications

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs

Prädiktive Regelung und Finite-Set-Beobachter für Windgeneratoren mit variabler Drehgeschwindigkeit

This dissertation presents several model predictive control (MPC) techniques and finite-position-set observers (FPSOs) for permanent-magnet synchronous generators and doubly-fed induction generators in variable-speed wind turbines. The proposed FPSOs are novel ones and based on the concept of finite-control-set MPC. Then, the problems of the MPC techniques like sensitivity to variations of the model parameters and others are investigated and solved in this work.Die vorliegende Dissertation stellt mehrere unterschiedliche Verfahren der modellprädiktiven Regelung (MPC) und so genannte Finite-Position-Set-Beobachter (FPSO) sowohl für Synchrongeneratoren mit Permanentmagneterregung als auch für doppelt gespeiste Asynchrongeneratoren in Windkraftanlagen mit variabler Drehzahl vor und untersucht diese. Für die Beobachter (FPSO) wird ein neuartiger Ansatz vorgestellt, der auf dem Konzept der Finite-Control-Set-MPC basiert. Außerdem werden typische Eigenschaften der MPC wie beispielsweise die Anfälligkeit gegenüber Parameterschwankungen untersucht und kompensiert

Speed -Sensorless Estimation And Position Control Of Induction Motors For Motion Control Applications

Thesis (Ph.D.) University of Alaska Fairbanks, 2006High performance sensorless position control of induction motors (IMs) calls for estimation and control schemes which offer solutions to parameter uncertainties as well as to difficulties involved with accurate flux and velocity estimation at very low and zero speed. In this thesis, novel control and estimation methods have been developed to address these challenges. The proposed estimation algorithms are designed to minimize estimation error in both transient and steady-state over a wide velocity range, including very low and persistent zero speed operation. To this aim, initially single Extended Kalman Filter (EKF) algorithms are designed to estimate the flux, load torque, and velocity, as well as the rotor, Rr' or stator, Rs resistances. The temperature and frequency related variations of these parameters are well-known challenges in the estimation and control of IMs, and are subject to ongoing research. To further improve estimation and control performance in this thesis, a novel EKF approach is also developed which can achieve the simultaneous estimation of R r' and Rs for the first time in the sensorless IM control literature. The so-called Switching and Braided EKF algorithms are tested through experiments conducted under challenging parameter variations over a wide speed range, including under persistent operation at zero speed. Finally, in this thesis, a sensorless position control method is also designed using a new sliding mode controller (SMC) with reduced chattering. The results obtained with the proposed control and estimation schemes appear to be very compatible and many times superior to existing literature results for sensorless control of IMs in the very low and zero speed range. The developed estimation and control schemes could also be used with a variety of the sensorless speed and position control applications, which are challenged by a high number of parameter uncertainties

Optimal speed and torque estimations for improving the DTC dynamic performance of induction machines

High-performance AC drives require accurate speed, flux, and torque estimations to provide a proper system operation. Thus, this thesis proposes a robust observer, i.e. Extended Kalman Filter (EKF), to offer optimal estimations of these components in order to improve the dynamic performance of Direct Torque Control (DTC) of induction motor drives. The selection and quality of EKF covariance elements have a considerable bearing on the effectiveness of motor drives. Many EKF-based optimization techniques involve only a single objective for the optimal estimation of speed without giving concern to the other variables. In addition, the optimization is performed on a complicated EKF structure. Nevertheless, in this study, both speed and torque are concurrently estimated. The work presents a new method to investigate the selection of EKF filters by using a Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) developed for resolving problems with multiobjectives. Filter element selection is the process of improving the concurrent estimation of speed and torque in order to increase EKF accuracy and allow higher drive efficiency. The proposed multi-optimal EKF-based estimation observer is used in combination with the sensorless direct torque control of induction motor. The investigated results for the multi-objective optimization indicate that the speed optimization gives superior performance when compared to the optimal torque. Owing to the large computation time of EKF algorithm, it increases the sampling time of DTC which leads to an increase in the motor torque ripples. The thesis proposes a Constant Frequency Torque Controller (CFTC) to replace the hysteresis torque controller that offers constant switching frequency and reduces torque ripples. Moreover, the CFTC has the capability of continuous switching regardless of speed variation; hence, leading to a consistent rotation of flux. Consequently, improvement on speed estimation, particularly at low and zero speed regions is accomplished and enhancement on the dynamic performance of torque is achieved when the reference speed change is applied from 0 rad/s, on the condition that the EKF observer is accurately optimized. To verify the improvements of the proposed methods, simulation and experimentation as well as comparison with the EKF-based DTC with the hysteresis controller are carried out

Stability analysis of the extended Kalman filter for Permanent Magnet Synchronous Motor

This paper presents a sensorless direct field oriented control fed interior permanent magnet synchronous motor (IPMSM) by using a known mathematical tool. The Kalman filter is an observer for linear and non-linear systems and is based on the stochastic intromission, in others words, noise. It is a question of studying the state and measurement noise covariance matrices Q and R on the stability of the Extended Kalman Filter. This last is used for the d, q stator current, mechanical speed, rotor position, stator resistance and the load torque estimation. The simulation tests carried out on Matlab Simulink showed that the matrix R improves much more quality of the estimated states while the matrix Q allows the improvement of the estimation process convergence

New Hybrid Sensorless Speed of a Non-Salient Pole PMSG Coupled to Wind turbine Using a Modified Switching Algorithm

©2019 ISA. Published by Elsevier Ltd. All rights reserved. his manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/The paper focuses on the design of position and speed observers for the rotor of a non-salient pole permanent magnet synchronous generator (NSPPMSG) coupled to a wind turbine. With the random nature of wind speed this observer is required to provide a position and speed estimates over a wide speed range. The proposed hybrid structure combines two observers and a switching algorithm to select the appropriate observer based on a modified weighting coefficients method. The first observer is a higher-order sliding mode observer (HOSMO) based on modified super twisting algorithm (STA) with correction term and operates in the medium and nominal wind speed ranges. The second observer is used in the low speed range and is based on the rotor flux estimation and the control by injecting a direct reference current different to zero. The stability of each observer has been successfully assessed using an appropriate Lyapunov function. The simulation results obtained show the effectiveness and performance of the proposed observer and control scheme.Peer reviewe

Genetic algorithm optimized robust nonlinear observer for a wind turbine system based on permanent magnet synchronous generator

© 2022 ISA. Published by Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.isatra.2022.02.004This paper presents an optimal control scheme for a Permanent Magnet Synchronous Generator (PMSG) coupled to a wind turbine operating without a position sensor. This sensorless scheme includes two observers: The first observer uses the flux to estimate the speed. However, an increase in the temperature or a degradation of the permanent magnet characteristics will result in a demagnetization of the machine causing a drop in the flux. The second observer is therefore used to estimate these changes in the flux from the speed and guaranties the stability of the system. This structure leads to a better exchange of information between the two observers, eliminates the problem of encoder and compensates for the demagnetization problem. To improve the precision of the speed estimator, the gain of the non-linear observer is optimized using Genetic Algorithm (GA) and the speed is obtained from a modified Phase Locked Loop (PLL) method using an optimized Sliding Mode Controller (SMC). Furthermore, to enhance the convergence speed of this observer scheme and improve the performance of the system a Fast Super Twisting Sliding Mode Control (FSTSMC) is introduced to reinforce the SMC strategy. A series of simulations are presented to show the effectiveness and robustness of proposed observer scheme.Peer reviewe

Speed Estimation for Indirect Field Oriented Control of Induction Motor Using Extended Kalman Filter

Speed sensors are required for the Field Oriented Control (FOC) of induction motors. These sensors reduce the sturdiness of the system and make it expensive. Therefore, a drive system without speed sensors is required. This paper presents a detailed study of the Extended Kalman Filter (EKF) for estimating the rotor speed of an Induction Motor (IM). Using MATLAB/SIMULINK software, a simulation model is built and tested. The simulation results illustrated and demonstrated the good performance and robustness of the EKF to estimate the high and low speed. Moreover, the performance of the EKF is found to be satisfactory in case there are externaldisturbances