NOVEL METHODS FOR PERMANENT MAGNET DEMAGNETIZATION DETECTION IN PERMANENT MAGNET SYNCHRONOUS MACHINES

Monitoring and detecting PM flux linkage is important to maintain a stable permanent magnet synchronous motor (PMSM) operation. The key problems that need to be solved at this stage are to: 1) establish a demagnetization magnetic flux model that takes into account the influence of various nonlinear and complex factors to reveal the demagnetization mechanism; 2) explore the relationship between different factors and demagnetizing magnetic field, to detect the demagnetization in the early stage; and 3) propose post-demagnetization measures. This thesis investigates permanent magnet (PM) demagnetization detection for PMSM machines to achieve high-performance and reliable machine drive for practical industrial and consumer applications. In this thesis, theoretical analysis, numerical calculation as well as experimental investigations are carried out to systematically study the demagnetization detection mechanism and post-demagnetization measures for permanent magnet synchronous motors. At first a flux based acoustic noise model is proposed to analyze online PM demagnetization detection by using a back propagation neural network (BPNN) with acoustic noise data. In this method, the PM demagnetization is detected by means of comparing the measured acoustic signal of PMSM with an acoustic signal library of seven acoustical indicators. Then torque ripple is chosen for online PM demagnetization diagnosis by using continuous wavelet transforms (CWT) and Grey System Theory (GST). This model is able to reveal the relationship between torque variation and PM electromagnetic interferences. After demagnetization being detected, a current regulation strategy is proposed to minimize the torque ripples induced by PM demagnetization. Next, in order to compare the demagnetization detection accuracy, different data mining techniques, Vold-Kalman filtering order tracking (VKF-OT) and dynamic Bayesian network (DBN) based detection approach is applied to real-time PM flux monitoring through torque ripple again. VKF-OT is introduced to track the order of torque ripple of PMSM running in transient state. Lastly, the combination of acoustic noise and torque is investigated for demagnetization detection by using multi-sensor information fusion to improve the system redundancy and accuracy. Bayesian network based multi-sensor information fusion is then proposed to detect the demagnetization ratio from the extracted features. During the analysis of demagnetization detection methods, the proposed PM detection approaches both form torque ripple and acoustic noise are extensively evaluated on a laboratory PM machine drive system under different speeds, load conditions, and temperatures

Modelling and detection of faults in axial-flux permanent magnet machines

The development of various topologies and configurations of axial-flux permanent magnet machine has spurred its use for electromechanical energy conversion in several applications. As it becomes increasingly deployed, effective condition monitoring built on reliable and accurate fault detection techniques is needed to ensure its engineering integrity. Unlike induction machine which has been rigorously investigated for faults, axial-flux permanent magnet machine has not. Thus in this thesis, axial-flux permanent magnet machine is investigated under faulty conditions. Common faults associated with it namely; static eccentricity and interturn short circuit are modelled, and detection techniques are established. The modelling forms a basis for; developing a platform for precise fault replication on a developed experimental test-rig, predicting and analysing fault signatures using both finite element analysis and experimental analysis. In the detection, the motor current signature analysis, vibration analysis and electrical impedance spectroscopy are applied. Attention is paid to fault-feature extraction and fault discrimination. Using both frequency and time-frequency techniques, features are tracked in the line current under steady-state and transient conditions respectively. Results obtained provide rich information on the pattern of fault harmonics. Parametric spectral estimation is also explored as an alternative to the Fourier transform in the steady-state analysis of faulty conditions. It is found to be as effective as the Fourier transform and more amenable to short signal-measurement duration. Vibration analysis is applied in the detection of eccentricities; its efficacy in fault detection is hinged on proper determination of vibratory frequencies and quantification of corresponding tones. This is achieved using analytical formulations and signal processing techniques. Furthermore, the developed fault model is used to assess the influence of cogging torque minimization techniques and rotor topologies in axial-flux permanent magnet machine on current signal in the presence of static eccentricity. The double-sided topology is found to be tolerant to the presence of static eccentricity unlike the single-sided topology due to the opposing effect of the resulting asymmetrical properties of the airgap. The cogging torque minimization techniques do not impair on the established fault detection technique in the single-sided topology. By applying electrical broadband impedance spectroscopy, interturn faults are diagnosed; a high frequency winding model is developed to analyse the impedance-frequency response obtained

Fault detection in dynamic conditions by means of discrete wavelet decomposition for PMSM running under bearing damage

This paper presents a study of the Permanent Magnet Synchronous Machine (PMSM) running under bearing damage. To carry out the study a two-dimensional (2-D) Finite Element Analysis (FEA) is used. Stator current induced harmonics for fault condition were investigated. Advanced signal analysis by means of Continuous and Discrete Wavelet Transforms was performed. Simulation were carried out and compared with experimental.Peer ReviewedPostprint (published version

Modeling And Analysis Of The Eds Maglev System With The Halbach Magnet Array

The magnetic field analysis based on the wavelet transform is performed. The Halbach array magnetic field analysis has been studied using many methods such as magnetic scalar potential, magnetic vector potential, Fourier analysis and Finite Element Methods. But these analyses cannot identify a transient oscillation at the beginning stage of levitation. The wavelet transform is used for analyzing the transient oscillatory response of an EDS Maglev system. The proposed scheme explains the under-damped dynamics that results from the cradle\u27s dynamic response to the irregular distribution of the magnetic field. It suggests this EDS Maglev system that responds to a vertical repulsive force could be subject to such instability at the beginning stage of a low levitation height. The proposed method is useful in analyzing instabilities at the beginning stage of levitation height. A controller for the EDS maglev system with the Halbach array magnet is designed for the beginning stage of levitation and after reaching the defined levitation height. To design a controller for the EDS system, two different stages are suggested. Before the object reaches a stable position and after it has reached a stable position. A stable position can be referred to as a nominal height. The former is the stage I and the latter is the stage II. At the stage I, to achieve a nominal height the robust controller is investigated. At the stage II, both translational and rotational motions are considered for the control design. To maintain system stability, damping control as well as LQR control are performed. The proposed method is helpful to understand system dynamics and achieve system stability

Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations

Paper VI is excluded from the dissertation until the article will be published.Permanent magnet synchronous motors (PMSMs) have played a key role in commercial and industrial applications, i.e. electric vehicles and wind turbines. They are popular due to their high efficiency, control simplification and large torque-to-size ratio although they are expensive. A fault will eventually occur in an operating PMSM, either by improper maintenance or wear from thermal and mechanical stresses. The most frequent PMSM faults are bearing faults, short-circuit and eccentricity. PMSM may also suffer from demagnetisation, which is unique in permanent magnet machines. Condition monitoring or fault diagnosis schemes are necessary for detecting and identifying these faults early in their incipient state, e.g. partial demagnetisation and inter-turn short circuit. Successful fault classification will ensure safe operations, speed up the maintenance process and decrease unexpected downtime and cost. The research in recent years is drawn towards fault analysis under dynamic operating conditions, i.e. variable load and speed. Most of these techniques have focused on the use of voltage, current and torque, while magnetic flux density in the air-gap or the proximity of the motor has not yet been fully capitalised. This dissertation focuses on two main research topics in modelling and diagnosis of faulty PMSM in dynamic operations. The first problem is to decrease the computational burden of modelling and analysis techniques. The first contributions are new and faster methods for computing the permeance network model and quadratic time-frequency distributions. Reducing their computational burden makes them more attractive in analysis or fault diagnosis. The second contribution is to expand the model description of a simpler model. This can be achieved through a field reconstruction model with a magnet library and a description of both magnet defects and inter-turn short circuits. The second research topic is to simplify the installation and complexity of fault diagnosis schemes in PMSM. The aim is to reduce required sensors of fault diagnosis schemes, regardless of operation profiles. Conventional methods often rely on either steady-state or predefined operation profiles, e.g. start-up. A fault diagnosis scheme robust to any speed changes is desirable since a fault can be detected regardless of operations. The final contribution is the implementation of reinforcement learning in an active learning scheme to address the imbalance dataset problem. Samples from a faulty PMSM are often initially unavailable and expensive to acquire. Reinforcement learning with a weighted reward function might balance the dataset to enhance the trained fault classifier’s performance.publishedVersio

Fast Magnetic Flux Line Allocation Algorithm for Interactive Visualization Using Magnetic Flux Line Existence Probability

The visualization of magnetic flux lines is one of the most effective ways to intuitively grasp a magnetic field. The depiction of continuous and smooth magnetic flux lines according to the magnetic field is of paramount importance. Thus, it is important to adequately allocate the distribution of magnetic flux lines in the analyzed space. The authors have already proposed two methods of determining the allocation of magnetic flux lines in 3-D space. However, both methods exhibited a long computation time to determine the allocation of magnetic flux lines. For solving this problem, in this paper, we propose a new improved method for correct allocation of magnetic flux lines in 3-D space with modest computational cost. The main advantages of this method are shorter computation time, correct allocation of the magnetic flux lines, and especially short computation time for visualization of magnetic flux lines when changes in the number of depicted flux lines is requested

Self-starting interior permanent magnet motor drive for electric submersible pumps

The interior permanent magnet (IPM) motor drive has evolved as the most energy efficient technology for modern motion control applications. Electric submersible pumps (ESPs) are electric motor driven fluid recovery systems. ESPs are widely used for producing oil and gas from deep downhole reservoirs. Standard ESPs are driven by classical squirrel cage induction motors (IMs) due to its self-starting capability from a balanced 3-phase ac excitation, ruggedness, simplicity, low cost and wide scale availability. Although there has been a tremendous growth in the design and development of highly efficient and reliable IPM motors for traction drive systems, application of the IPM motor technology in ESPs is still in its infancy due to challenges associated with the design and control of IPM motors. In this thesis, a new self-starting, efficient and reliable IPM motor drive technology is proposed for ESP systems to extend their efficiency, longevity and performance. This thesis investigates two different types of self-starting interior permanent magnet (IPM) motors: cage-equipped IPM motors known as line-start IPM motors and a new type of hybrid self-starting motors called hysteresis IPM motors. The limited synchronization capability of line-start IPM motors for high inertial loads is explained in this thesis. To overcome the starting and synchronization problems associated with line-start IPM motors, a new type of hybrid hysteresis IPM motor is proposed in this thesis. Equivalent circuit modeling and finite element analysis of hysteresis IPM motors are carried out in this thesis. A prototype 2.5 kW hysteresis IPM motor is constructed and experimentally tested in the laboratory. In order to limit the inrush current during starting, a stable soft starter has been designed, simulated and implemented for variable speed operations of the motor. The simulation and experimental results are presented and analyzed in this thesis. Self-starting IPM motors suffer from hunting induced torsional oscillations. Electric submersible pumps are vulnerable against sustained hunting and can experience premature failures. In this thesis, a novel stator current signature based diagnostic system for detection of torsional oscillations in IPM motor drives is proposed. The diagnostic system is non-intrusive, fast and suitable for remote condition monitoring of an ESP drive system. Finally, a position sensorless control technique is developed for an IPM motor drive operated from an offshore power supply. The proposed technique can reliably start and stabilize an IPM motor using a back-emf estimation based sensorless controller. The efficacy of the developed sensorless control technique is investigated for a prototype 3-phase, 6-pole, 480V, 10-HP submersible IPM motor drive. In summary, this thesis carried out modeling, analysis and control of different types of self-starting IPM motors to assess their viability for ESP drive systems. Different designs of self-starting IPM motors are presented in this thesis. In future, a fully scalable self-starting IPM motor drive will be designed and manufactured that can meet the industrial demands for high power, highly reliable and super-efficient ESP systems

Performance of Induction Machines

Induction machines are one of the most important technical applications for both the industrial world and private use. Since their invention (achievements of Galileo Ferraris, Nikola Tesla, and Michal Doliwo-Dobrowolski), they have been widely used in different electrical drives and as generators, thanks to their features such as reliability, durability, low price, high efficiency, and resistance to failure. The methods for designing and using induction machines are similar to the methods used in other electric machines but have their own specificity. Many issues discussed here are based on the fundamental achievements of authors such as Nasar, Boldea, Yamamura, Tegopoulos, and Kriezis, who laid the foundations for the development of induction machines, which are still relevant today. The control algorithms are based on the achievements of Blaschke (field vector-oriented control) and Depenbrock or Takahashi (direct torque control), who created standards for the control of induction machines. Today’s induction machines must meet very stringent requirements of reliability, high efficiency, and performance. Thanks to the application of highly efficient numerical algorithms, it is possible to design induction machines faster and at a lower cost. At the same time, progress in materials science and technology enables the development of new machine topologies. The main objective of this book is to contribute to the development of induction machines in all areas of their applications