Control Strategies for Open-End Winding Drives Operating in the Flux-Weakening Region

This paper presents and compares control strategies for three-phase open-end winding drives operating in the flux-weakening region. A six-leg inverter with a single dc-link is associated with the machine in order to use a single energy source. With this topology, the zero-sequence circuit has to be considered since the zero-sequence current can circulate in the windings. Therefore, conventional over-modulation strategies are not appropriate when the machine enters in the flux-weakening region. A few solutions dealing with the zero-sequence circuit have been proposed in literature. They use a modified space vector modulation or a conventional modulation with additional voltage limitations. The paper describes the aforementioned strategies and then a new strategy is proposed. This new strategy takes into account the magnitudes and phase angles of the voltage harmonic components. This yields better voltage utilization in the dq frame. Furthermore, inverter saturation is avoided in the zero-sequence frame and therefore zero-sequence current control is maintained. Three methods are implemented on a test bed composed of a three-phase permanent-magnet synchronous machine, a six-leg inverter and a hybrid DSP/FPGA controller. Experimental results are presented and compared for all strategies. A performance analysis is conducted as regards the region of operation and the machine parameters.Projet SOFRACI/FU

Observer-based Fault Detection and Diagnosis for Mechanical Transmission Systems with Sensorless Variable Speed Drives

Observer based approaches are commonly embedded in sensorless variable speed drives for the purpose of speed control. It estimates system variables to produce errors or residual signals in conjunction with corresponding measurements. The residual signals then are relied to tune control parameters to maintain operational performance even if there are considerable disturbances such as noises and component faults. Obviously, this control strategy outcomes robust control performances. However, it may produce adverse consequences to the system when faults progress to high severity. To prevent the occurrences of such consequences, this research proposes the utilisation of residual signals as detection features to raise alerts for incipient faults. Based on a gear transmission system with a sensorless variable speed drive (VSD), observers for speed, flux and torque are developed for examining their residuals under two mechanical faults: tooth breakage with different degrees of severities and shortage of lubricant at different levels are investigated. It shows that power residual signals can be based on to indicate different faults, showing that the observer based approaches are effective for monitoring VSD based mechanical systems. Moreover, it also shows that these two types fault can be separated based on the dynamic components in the voltage signals

Improved direct torque control using Kalman filter: application to a doubly-fed machine

Direct Torque Control (DTC) has been extensively researched and applied during the last two decades. However, it has only first been applied to the Brushless Doubly Fed Reluctance Machine (BDFRM) a few years ago in its basic form inheriting its intrinsic flux estimation problems that propagate throughout the algorithm and hence compromise the DTC performance. In this paper, we propose the use of Kalman Filter (KF) as an alternative to improve the estimation and consequently the control performance of the DTC. The KF is designed around a nominal model, but is shown to be reliable over the whole operating range of the BDFRM. Moreover, we use a modified robust exact differentiator based on Sliding Mode (SM) techniques to calculate the angular velocity from an angular position encoder. Computer simulations are meticulously designed to take into account real-world physical constraints and thus show illustrative supporting results as expected from an experimental setup

To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Condition monitoring wind turbine gearboxes using on-line/in-line oil analysis techniques

Paper examining condition monitoring wind turbine gearboxes using on-line/in-line oil analysis techniques

Vibration-based adaptive novelty detection method for monitoring faults in a kinematic chain

Postprint (published version

H-G Diagram Based Rotor Parameters Identification for Induction Motors Thermal Monitoring

International audienceIn this paper, an effective on-line method for induction motor parameter identification, especially rotor parameters, based on the H-G diagram is presented for motor thermal monitoring purpose. The H-G diagram is established from the analysis of the induction motor measurement of active and reactive power consumption for each operating point. Computer simulations and experimental tests, carried out for a 4-kW four-pole squirrel cage induction motor, provide an encouraging validation of the proposed thermal monitoring technique. The process should be refined for a possible industrial application

Detailed state of the art review for the different on-line/in-line oil analysis techniques in context of wind turbine gearboxes

The main driver behind developing advanced condition monitoring (CM) systems for the wind energy industry is the delivery of improved asset management regarding the operation and maintenance of the gearbox and other wind turbine components and systems. Current gearbox CM systems mainly detect faults by identifying ferrous materials, water, and air within oil by changes in certain properties such as electrical fields. In order to detect oil degradation and identify particles, more advanced devices are required to allow a better maintenance regime to be established. Current technologies available specifically for this purpose include Fourier transform infrared (FTIR) spectroscopy and ferrography. There are also several technologies that have not yet been or have been recently applied to CM problems. After reviewing the current state of the art, it is recommended that a combination of sensors would be used that analyze different characteristics of the oil. The information individually would not be highly accurate but combined it is fully expected that greater accuracy can be obtained. The technologies that are suitable in terms of cost, size, accuracy, and development are online ferrography, selective fluorescence spectroscopy, scattering measurements, FTIR, photoacoustic spectroscopy, and solid state viscometers

Stochastic modeling error reduction using Bayesian approach coupled with an adaptive Kriging based model

Purpose - Magnetic material properties of an electromagnetic device (EMD) can be recovered by solving a coupled experimental numerical inverse problem. In order to ensure the highest possible accuracy of the inverse problem solution, all physics of the EMD need to be perfectly modeled using a complex numerical model. However, these fine models demand a high computational time. Alternatively, less accurate coarse models can be used with a demerit of the high expected recovery errors. The purpose of this paper is to present an efficient methodology to reduce the effect of stochastic modeling errors in the inverse problem solution. Design/methodology/approach - The recovery error in the electromagnetic inverse problem solution is reduced using the Bayesian approximation error approach coupled with an adaptive Kriging-based model. The accuracy of the forward model is assessed and adapted a priori using the cross-validation technique. Findings - The adaptive Kriging-based model seems to be an efficient technique for modeling EMDs used in inverse problems. Moreover, using the proposed methodology, the recovery error in the electromagnetic inverse problem solution is largely reduced in a relatively small computational time and memory storage. Originality/value - The proposed methodology is capable of not only improving the accuracy of the inverse problem solution, but also reducing the computational time as well as the memory storage. Furthermore, to the best of the authors knowledge, it is the first time to combine the adaptive Kriging-based model with the Bayesian approximation error approach for the stochastic modeling error reduction