A Review of Transverse Flux Machines Topologies and Design

High torque and power density are unique merits of transverse flux machines (TFMs). TFMs are particularly suitable for use in direct-drive systems, that is, those power systems with no gearbox between the electric machine and the prime mover or load. Variable speed wind turbines and in-wheel traction seem to be great-potential applications for TFMs. Nevertheless, the cogging torque, efficiency, power factor and manufacturing of TFMs should still be improved. In this paper, a comprehensive review of TFMs topologies and design is made, dealing with TFM applications, topologies, operation, design and modeling

Magnetic Material Modelling of Electrical Machines

The need for electromechanical energy conversion that takes place in electric motors, generators, and actuators is an important aspect associated with current development. The efficiency and effectiveness of the conversion process depends on both the design of the devices and the materials used in those devices. In this context, this book addresses important aspects of electrical machines, namely their materials, design, and optimization. It is essential for the design process of electrical machines to be carried out through extensive numerical field computations. Thus, the reprint also focuses on the accuracy of these computations, as well as the quality of the material models that are adopted. Another aspect of interest is the modeling of properties such as hysteresis, alternating and rotating losses and demagnetization. In addition, the characterization of materials and their dependence on mechanical quantities such as stresses and temperature are also considered. The reprint also addresses another aspect that needs to be considered for the development of the optimal global system in some applications, which is the case of drives that are associated with electrical machines

Node mapping criterion for highly saturated interior PMSMs using magnetic reluctance network

Interior Permanent Magnet Synchronous Machine (IPMSM) are high torque density machines that usually work under heavy load conditions, becoming magnetically saturated. To obtain properly their performance, this paper presents a node mapping criterion that ensure accurate results when calculating the performance of a highly saturated IPMSM via a novel magnetic reluctance network approach. For this purpose, a Magnetic Circuit Model (MCM) with variable discretization levels for the different geometrical domains is developed. The proposed MCM caters to V-shaped IPMSMs with variable magnet depth and angle between magnets. Its structure allows static and dynamic time stepping simulations to be performed by taking into account complex phenomena such as magnetic saturation, cross-coupling saturation effect and stator slotting effect. The results of the proposed model are compared to those obtained by Finite Element Method (FEM) for a number of IPMSMs obtaining excellent results. Finally, its accuracy is validated comparing the calculated performance with experimental results on a real prototype

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Hybrid Reluctance Machine with Skewed Permanent Magnets and Zero-Sequence Current Excitation

The reluctance machine is a potential candidate for electrical vehicle propulsion because of its reliable structure, low cost, flexible flux regulation ability, and wide speed range. However, the torque density is unsatisfactory because of the poor excitation ability and low stator core utilization factor. To solve this problem, in this paper, a novel hybrid reluctance machine (HRM) with the skewed permanent magnet (PM) and the zero-sequence current is proposed for electric vehicles. The skewed PM has two magnetomotive force (MMF) components with different functions. The radial MMF component provides extra torque by the flux modulation effect. The tangential MMF component can generate a constant biased field in the stator core to relieve the saturation caused by the zero-sequence current and thus improve the utilization factor of the stator core. Therefore, torque improvement and the relief of stator core saturation can be simultaneously achieved by the skewed PM. In this paper, the machine structure and principle of the proposed machine are introduced. And ultimately, the machine’s electromagnetic performances are evaluated under different PM magnetization directions and zero-sequence current angles by using finite element analysis (FEA)

Axial Stress Analysis and Comparison of the Novel Dual 3-phase Axial Flux Permanent Magnet Machines

In previous research, a novel three-phase dual-stator axial flux permanent magnet machine characterized by the advantages of compact structure and low moment of inertia is proposed for industrial robot application. In order to improve its functional reliability furtherly, the dual three-phase axial flux permanent magnet machine (DTP-AFPM) is firstly proposed. Benefiting from the combination of 12 slots/10 poles, the coil configuration of one stator disk can be modified to a dual three phase full-pitch winding straightforwardly, as a result, one kind of the DTP-AFPMs is achieved which is named as the no shift model in this paper. For eliminating the coil reconfiguration on each stator disk and the connection of the coils belonging to the same phase between two stator disks, the shift model which is based on the shift of the two stator disks to obtain the phasor difference between two three-phase windings is introduced. The characteristics and electromagnetic performances of these two models are analyzed and compared. However, it should be noted that the light-weight disk-type rotor of DTP-AFPMs also degrade the strength of the rotor. The proposed DTP-AFPMs are more sensitive to the axial stress on the rotor which introduces not only the vibration and noise but also the deformation or even the damage of the rotor. Thus, the axial stress on the rotor is investigated and treated as a critical evaluation indicator. The axial stress is analyzed under both healthy and fault conditions and its distribution on the rotor is given on a 2-D plane