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

Effects of Winding Connection on Performance of a Six-Phase Switched Reluctance Machine

This paper investigates the effect of the stator winding connection on the performance of a six-phase Switched Reluctance Machine (SRM). Five winding connection types are proposed for the machine. Finite element analyses (FEAs) of flux distribution, output torque and core losses are presented under single-phase and multi-phase excitation for each connection and the results are used to compare the average torque and torque ripple ratio characteristics and to develop understanding of the respective contributions of mutual inductance in torque development. Experimental tests on a six-phase conventional SRM verify the torque performance and mutual inductance effects of the different winding connections. An optimum winding configuration for a six-phase SRM is proposed

Improved method for the scalar control of induction motor drives

Many control schemes have been proposed for induction motors, which are in themselves highly complex non-linear and sometimes internally unstable systems.One of themost accurate control schemes is encodered rotor flux orientated vector control. The advantages and disadvantages of this control are well known and several variations, or reduced vector schemes, have been proposed. This study introduces an improved encoderless scalar, or approximated vector, control method for induction machines which can be applied to general purpose applications that do not require the most precise control. The proposed method overcomes practical difficulties and is suitable for industrial applications. The slip compensated stator flux linkage oriented scheme proposed in this study does not require flux estimation or a speed sensor, only requiring nameplate data, stator current and stator resistance measurement, which can easily be determined at start-up. Simulation and experimental investigations including field weakening operation and the effect of stator resistance variation demonstrate the improved performance of the new scheme compared to previous open loop V/Hz and stator resistive compensated schemes especially at low rotor speeds

A torque ripple minimization method for six-phase switched reluctance motor drives

This paper presents a direct torque control (DTC) method on a six-phase SRM driven by a six-phase asymmetric half bridge converter. Modeling and simulations of the proposed drive system have been built with MATLAB/SIMULINK. In the proposed DTC method, instantaneous output torque of the six-phase SRM is directly controlled by flux-linkage magnitude and rotating speed regulation (acceleration or deceleration) respective to rotor movement. The simulation and test results accurately reflect the actual operation states of the SRM. Compared with traditional current chopping control (CCC), the DTC method can effectively reduce the torque ripple for the six-phase SRM

Improved rotor flux estimation at low speeds for torque MRAS-based sensorless induction motor drives

In this paper, an improved rotor flux estimation method for the Torque model reference adaptive schemes (TMRAS) sensorless induction machine drive is proposed to enhance its performance in low and zero speed conditions. The conventional TMRAS scheme uses an open loop flux estimator and a feedforward term, with basic low pass filters replacing the pure integrators. However, the performance of this estimation technique has drawbacks at very low speeds with incorrect flux estimation significantly affecting this inherently sensorless scheme. The performance of the proposed scheme is verified by both simulated and experimental testing for an indirect vector controlled 7.5-kW induction machine. Results show the effectiveness of the proposed estimator in the low- and zero-speed regions with improved robustness against motor parameter variation compared to the conventional method

Computationally Efficient Self-Tuning Controller for DC-DC Switch Mode Power Converters Based on Partial Update Kalman Filter

In this paper, a partial update Kalman Filter (PUKF) is presented for the real-time parameter estimation of a DC-DC switch-mode power converter (SMPC). The proposed estimation algorithm is based on a novel combination between the classical Kalman filter and an M-Max partial adaptive filtering technique. The proposed PUKF offers a significant reduction in computational effort compared to the conventional implementation of the Kalman Filter (KF), with 50% less arithmetic operations. Furthermore, the PUKF retains comparable overall performance to the classical KF. To demonstrate an efficient and cost effective explicit self-tuning controller, the proposed estimation algorithm (PUKF) is embedded with a Bányász/Keviczky PID controller to generate a new computationally light self-tuning controller. Experimental and simulation results clearly show the superior dynamic performance of the explicit self-tuning control system compared to a conventional pole placement design based on a pre-calculated average model

Submodule Voltage Estimation Scheme in Modular Multilevel Converters with Reduced Voltage Sensors Based on Kalman Filter Approach

This paper presents a new voltage estimation method for the submodule (SM) capacitor in a modular multilevel converter (MMC). The proposed method employs a Kalman filter (KF) algorithm to estimate the SM voltages of the converter. Compared with sensor-based methods, this scheme requires only one voltage sensor to achieve the voltage-balancing of the converter. This sensor is connected to the total arm voltage; the proposed algorithm requires also the switching patterns of each upper SM switch which are provided by the controller used without the need for extra sensors. The substantial reduction in the number of voltage sensors improves the system reliability and decreases its cost and complexity. Extensive simulation and experimental analyses carried out to validate the proposed estimation scheme under different conditions include steady-state analyses, the effect of variations in capacitance and inductance, of the impact of low carrier and effective switching frequency on the accuracy of the estimation, step changes to the load, and a range changes in DC voltage. The results obtained are experimentally verified using a single-phase MMC

Real-time parameter estimation of DC-DC converters using a self-tuned kalman filter

To achieve high-performance control of modern dc-dc converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. In this paper, a new parametric system identification method, based on a Kalman filter (KF) approach is introduced to estimate the discrete model of a synchronous dc-dc buck converter. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. The proposed KF estimation technique is experimentally verified using a Texas Instruments TMS320F28335 microcontroller platform and synchronous step-down dc-dc converter. Results demonstrate a robust and reliable real-time estimator. The proposed method can accurately identify the discrete coefficients of the dc-dc converter. This paper also validates the performance of the identification algorithm with time-varying parameters, such as an abrupt load change. The proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Furthermore, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical exponentially weighted recursive least-squares method

On the identifiability of steady-state induction machine models using external measurements

A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the alternating conditional expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1-kW three-phase induction machine in conjunction with the Levenberg-Marquardt algorithm, which is developed and applied here for this purpose

Capacitor Voltage Estimation Scheme with Reduced Number of Sensors for Modular Multilevel Converters

This paper presents a new method to measure the voltage across the submodule (SM) capacitors in a modular multilevel converter (MMC). The proposed technique requires only one voltage sensor per arm. This reduces the number of sensors required compared to conventional sensor-based methods. Therefore, the cost and complexity of the system are reduced, which in turn improves the converter’s overall reliability. The proposed method employs an exponentially weighted recursive least square (ERLS) algorithm to estimate the SM capacitor voltages through the measured total arm voltage and the switching patterns of each SM. There is thus no need for extra sensors to measure these control signals as they are directly provided from the controller. The robustness of the proposed method is confirmed via introducing deviations for the capacitance values, dynamic load changes, DC voltage change and start-up transient condition. Simulation and experimentally validated results based on a single-phase MMC show the effectiveness of the proposed method in both, steady-state and dynamic operations