Investigation of rotor position detection schemes for PMSM drives based on analytical machine model incorporating nonlinear saliencies

University of Technology, Sydney. Faculty of Engineering and Information Technology.This thesis presents the essential and new improvements of the machine modelling and drive-strategies for permanent magnet synchronous machines (PMSMs), including the rotor position sensorless drive schemes. Many important issues about PMSM drive schemes, from the modelling to drive design have been investigated from the machine model point of view. A comprehensive PMSM model incorporating both structural and magnetic saturation saliencies has been developed and expressed numerically and analytically. Highly efficient rotor position detection method has been developed based on the new machine model. The traditional mathematical model of PMSM is investigated at the beginning of this thesis for the conventional PMSM drive schemes, including six-step control, field oriented control (FOC) and direct torque control (DTC). The fundamental principles and improvements of the drives are summarized based on the machine model. Performance companson is conducted for djfferent schemes and an improved DTC scheme is developed. PMSM drive without rotor position sensor, or so called sensorless drive, is a desired feature for the electrical servo systems and automotive applications. The Jack of accurate nonlinear machine model L the bottle neck for highly efficient sensorless drive development. Experiment trial and error attempts have to be employed to design rotor position detection schemes. On the other hand, there is not a comprehensive machine model to assess the sensorless drive performance. The inaccuracies associated with the conventional PMSM model have been discussed in this thesis. The saliencies in PMSM utilized for rotor position tracking are classified as two types, the structural saliency and the magnetic saturation saliency. The nonlinear saturation saliency cannot be modelled in the conventional PMSM model. However, it is essential for the rotor position estimation, especially the initial rotor position detection. A composite function is designed to express the inductances of PMSM, incorporating the nonlinear saturation saliency. Experimentally collected inductance data are used to regress the parameter matrix and a numerical PMSM model is developed. It is then proved that the structural and saturation saliencies can be decoupled analytically and expressed separately. The concepts, structural saliency ratio and saturation saliency ratio are defined to indicate the magnetic saliency in PMSMs. The analytical mathematic machine model incorporates the nonlinear behaviour of the magnetic field of PMSMs. The proposed model is validated by the experimentally collected data. Based on the developed analytical nonlinear machine model, a DC voltage pulse injection based initial rotor position detection scheme is designed and implemented. Thanks to the comprehensive machine model, improved injection scheme is designed to minimize the rotor vibration and increase the estimation speed. Simulation and experiment of the novel detection method are conducted to verify the estimation accuracy. Finally the proposed model is applied to analyse the sensorless drive schemes for PMSMs. The investigation focuses on high frequency signal injection based sensorless methods. A new inj ection method is proposed based on the machine model, in which the carrier signal is injected on a fixed stator spatial direction. The proposed sensorless method and other reported sensorless schemes are compared based on both machine model and simulation. According to the non1inear machine model, generalized indicators are defined to express the drive performance, computing cost and estimation efficiency, which provide a comprehensive assessment for sensorless drive schemes of PMSM

Investigating the Effect of Water Contamination on Gearbox Lubrication based on Motor Control Data from a Sensorless Drive

Water is one of the most significant destructive contaminations to lubricants which in turn lead to more power consumption and early damage to rotating machines. This study explores the effect of water contents in gearbox lube oil on the responses of electrical supply parameters. A two stage gearbox based mechanical transmission system driven by a sensorless variable speed drive (VSD) is utilised to investigate experimentally any measurable changes in these signals that can be correlated with water contamination levels. Results show that the supply parameters obtained from both external measurements and the VSD control data can be correlated to the contamination levels of oil with water and hence can be based on for an instant diagnosis of water contamination. Particularly, the voltage and hence the power responses are more sensitive to the water contents than that of current because the VSD regulates more the voltage to adapt the small load changes due to the water induced lubrication degradation. Simultaneously, vibration also shows changes which agree with that of power supply parameters

K-nearest Neighbor Search by Random Projection Forests

K-nearest neighbor (kNN) search has wide applications in many areas, including data mining, machine learning, statistics and many applied domains. Inspired by the success of ensemble methods and the flexibility of tree-based methodology, we propose random projection forests (rpForests), for kNN search. rpForests finds kNNs by aggregating results from an ensemble of random projection trees with each constructed recursively through a series of carefully chosen random projections. rpForests achieves a remarkable accuracy in terms of fast decay in the missing rate of kNNs and that of discrepancy in the kNN distances. rpForests has a very low computational complexity. The ensemble nature of rpForests makes it easily run in parallel on multicore or clustered computers; the running time is expected to be nearly inversely proportional to the number of cores or machines. We give theoretical insights by showing the exponential decay of the probability that neighboring points would be separated by ensemble random projection trees when the ensemble size increases. Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.Comment: 15 pages, 4 figures, 2018 IEEE Big Data Conferenc

FPGA-based implementation of the back-EMF symmetric-threshold-tracking sensorless commutation method for brushless DC-machines

The operation of brushless DC permanent-magnet machines requires information of the rotor position to steer the semiconductor switches of the power-supply module which is commonly referred to as Brushless Commutation. Different sensorless techniques have been proposed to estimate the rotor position using current and voltage measurements of the machine. Detection of the back-electromotive force (EMF) zero-crossing moments is one of the methods most used to achieve sensorless control by predicting the commutation moments. Most of the techniques based on this phenomenon have the inherit disadvantage of an indirect detection of commutation moments. This is the result of the commutation moment occurring 30 electrical degrees after the zero-crossing of the induced back-emf in the unexcited phase. Often, the time difference between the zero crossing of the back-emf and the optimal current commutation is assumed constant. This assumption can be valid for steady-state operation, however a varying time difference should be taken into account during transient operation of the BLDC machine. This uncertainty degrades the performance of the drive during transients. To overcome this problem which improves the performance while keeping the simplicity of the back-emf zero-crossing detection method an enhancement is proposed. The proposed sensorless method operates parameterless in a way it uses none of the brushless dc-machine parameters. In this paper different aspects of experimental implementation of the new method as well as various aspects of the FPGA programming are discussed. Proposed control method is implemented within a Xilinx Spartan 3E XC3S500E board