Optimized Kalman filters for sensorless vector control induction motor drives

This paper presents the comparison between optimized unscented Kalman filter (UKF) and optimized extended Kalman filter (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF and EKF depends on the accurate selection of state and noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find the optimal values of state and noise covariance matrices. The main objectives of genetic algorithm to be minimized are the mean square errors (MSE) between actual and estimation of speed, current, and flux. Simulation results show the optimal state and noise covariance matrices can improve the estimation of speed, current, torque, and flux in sensorless DFOCIM drive. Furthermore, optimized UKF present higher performance of state estimation than optimized EKF under different motor operating conditions

Pulse-width modulation direct torque control induction motor drive with Kalman filter

The paper deals with application of Kalman filter in induction motor drive using pulse-width modulation direct torque control (PWM-DTC). In the first part, the conventional PWM-DTC drive is described and Kalman filter is utilized to filter components of stator current vector those are assumed to be disturbed by white noise. The second part contains simulation results that are obtained in different cases of load torque, process and measurement noise covariances. The integral time absolute error (ITAE) performance index, undershoot, ripple of important quantities are used to compare the conventional drive structure and proposed drive structure with Kalman filter. The simulation results confirm the expected dynamic response of the proposed structure

Non-Linear Estimation using the Weighted Average Consensus-Based Unscented Filtering for Various Vehicles Dynamics towards Autonomous Sensorless Design

The concerns to autonomous vehicles have been becoming more intriguing in coping with the more environmentally dynamics non-linear systems under some constraints and disturbances. These vehicles connect not only to the self-instruments yet to the neighborhoods components, making the diverse interconnected communications which should be handled locally to ease the computation and to fasten the decision. To deal with those interconnected networks, the distributed estimation to reach the untouched states, pursuing sensorless design, is approached, initiated by the construction of the modified pseudo measurement which, due to approximation, led to the weighted average consensus calculation within unscented filtering along with the bounded estimation errors. Moreover, the tested vehicles are also associated to certain robust control scenarios subject to noise and disturbance with some stability analysis to ensure the usage of the proposed estimation algorithm. The numerical instances are presented along with the performances of the control and estimation method. The results affirms the effectiveness of the method with limited error deviation compared to the other centralized and distributed filtering. Beyond these, the further research would be the directed sensorless design and fault-tolerant learning control subject to faults to negate the failures.Comment: 13 pages, 33 figure

PSO Based EKF Wheel-rail Adhesion Estimation

An ideal traction and braking system not only ensures ride comfort and transportation safety but also attracts significant cost benefits through reduction of damaging processes in wheel-rail and optimum on-time operation. In order to overcome the problem of the wheel slip/slide at the wheel-rail contact surface, detection of adhesion and its changes has high importance and scientifically challenging, because adhesion is influenced by different factors. However, critical information this detection provides is applicable not only in the control of trains to avoid undesirable wear of the wheels/track but also the safety compromise of rail operations. The adhesion level between the wheel and rail cannot be measured directly but the friction on the rail surface can be measured using measurement techniques. Estimation of wheel-rail adhesion conditions during railway operations can characterize the braking and traction control system. This paper presents the particle swarm optimization (PSO) based Extended Kalman Filter (EKF) to estimate adhesion force. The main limitation in applying EKF to estimate states and parameters is that its optimality is critically dependent on the proper choice of the state and measurement noise covariance matrices. In order to overcome the mentioned difficulty, a new approach based on the use of the tuned EKF is proposed to estimate induction motor (as a main part of the train moving system) parameters. This approach consists of two steps: In the first step the covariance matrices are optimized by PSO and then, their values will be introduced in the estimation loop.

Induction Motor Performance Improvement using Super Twisting SMC and Twelve Sector DTC

Induction motor (IM) direct torque control (DTC) is prone to a number of weaknesses, including uncertainty, external disturbances, and non-linear dynamics. Hysteresis controllers are used in the inner loops of this control method, whereas traditional proportional-integral (PI) controllers are used in the outer loop. A high-performance torque and speed system is consequently needed to assure a stable and reliable command that can tolerate such unsettled effects. This paper treats the design of a robust sensorless twelve-sector DTC of a three-phase IM. The speed controller is conceived based on high-order super-twisting sliding mode control with integral action (iSTSMC). The goal is to decrease the flux, torque, the current ripples that constitute the major conventional DTC drawbacks. The phase current ripples have been effectively reduced from 76.92% to 45.30% with a difference of 31.62%. A robust adaptive flux and speed observer-based fuzzy logic mechanism are inserted to get rid of the mechanical sensor. Satisfactory results have been got through simulations in MATLAB/Simulink under load disturbance. In comparison to a conventional six-sector DTC, the suggested technique has a higher performance and lower distortion rate

Artificial intelligence applied to speed sensorless induction motor drives

During the last two decades there has been considerable development of sensorless vector controlled induction motor drives for high performance industrial applications. Such control strategies reduce the drive's cost, size and maintenance requirements while increasing the system's reliability and robustness. Parameter sensitivity, high computational effort and instability at low and zero speed can be the main shortcomings of sensorless control. Sensorless drives have been successfully applied for medium and high speed operation, but low and zero speed operation is still a critical problem. Much recent research effort is focused on extending the operating region of sensorless drives near zero stator frequency. Several strategies have been proposed for rotor speed estimation in sensorless induction motor drives based on the machine fundamental excitation model. Among these techniques Model Reference Adaptive Systems (MRAS) schemes are the most common strategies employed due to their relative simplicity and low computational effort. Rotor flux-MRAS is the most popular MRAS strategy and significant attempts have been made to improve the performance of this scheme at low speed. Artificial Intelligence (AI) techniques have attracted much attention in the past few years as powerful tools to solve many control problems. Common AI strategies include neural networks, fuzzy logic and genetic algorithms. The mam purpose of this work is to show that AI can be used to improve the sensorless performance of the well-established MRAS observers in the critical low and zero speed region of operation. This thesis proposes various novel methods based on AI combined with MRAS observers. These methods have been implemented via simulation but also on an experimental drive based around a commercial induction machine. Detailed simulations and experimental tests are carried out to investigate the performance of the proposed schemes when compared to the conventional rotor fluxMRAS. Various schemes are implemented and tested in real time using a 7.5 kW induction machine and a dSP ACE DS 1103 controller board. The results presented for these new schemes show the great improvement in the performance of the MRAS observer in both open loop and sensorless modes of operation at low and zero speed.EThOS - Electronic Theses Online ServiceMinistry of Higher Education, Arab Republic of EgyptGBUnited Kingdo

Industrial and Technological Applications of Power Electronics Systems

The Special Issue "Industrial and Technological Applications of Power Electronics Systems" focuses on: - new strategies of control for electric machines, including sensorless control and fault diagnosis; - existing and emerging industrial applications of GaN and SiC-based converters; - modern methods for electromagnetic compatibility. The book covers topics such as control systems, fault diagnosis, converters, inverters, and electromagnetic interference in power electronics systems. The Special Issue includes 19 scientific papers by industry experts and worldwide professors in the area of electrical engineering

Dynamic Data Assimilation

Data assimilation is a process of fusing data with a model for the singular purpose of estimating unknown variables. It can be used, for example, to predict the evolution of the atmosphere at a given point and time. This book examines data assimilation methods including Kalman filtering, artificial intelligence, neural networks, machine learning, and cognitive computing