Rare-Earth Permanent Magnets (RE-PMs) are widely used in high efficiency and high torque density electrical machines. However, in recent years the exclusive and unstable supply of Rare-Earth Elements (REEs) is a major concern to substitute RE-based electrical machines by less or even no RE alternatives. Ferrite (Fe) PMs as the least expensive PMs show promising opportunities for designing low-cost non-RE electrical machines. This paper reviews some relevant works on Fe-based machines which generally have been conducted to replace RE-based machines. The Fe-based machines including PM brushless, PM-assisted synchronous reluctance, PM flux switching, and PM Vernier machines are introduced and their main design challenges, such as low PM energy product and vulnerability to irreversible demagnetization are addressed. In addition, a comparison of RE-based and Fe-based machines reveals that when both cost and efficiency are the major concerns, Fe-based machines can be a good successor to RE-based machines.

Wave energy is one of the most attractive forms of renewable energy. The reasons include its promising availability, predictability, persistence, and power density. This study focuses on all linear generator designs and technologies which have been used so far in direct‐drive wave energy converters (DD‐WECs). Currently, linear permanent magnet generators (LPMG) have been proposed as the most advantageous generator system developed for DD‐WECs. After a brief description of linear generator based wave energy converters, all proposed state‐of‐the‐art of LPMG topologies available in the literature are discussed and compared in terms of flux path, core type, location of PMs, and etc. In addition, other linear generator technologies such as linear switched reluctance and linear superconducting generators, as an alternative to LPMGs, are reviewed. Finally, based on the surveyed quantitative comparisons performed in previous works, eight major concepts are evaluated in terms of economic and operational aspects.

There is always uncertainty in industrial manufacturing. These uncertainties have an undesirable impact on the products if deterministic optimization approaches are employed. In order to have products as desired, uncertainties must be quantified in the design process. This paper presents a robust design optimization of an outer rotor surface mounted permanent magnet motor with particular application in the hybrid vehicle using the design for six-sigma methodology. Due to very long computational time of robust optimization, a ten high-dimensional surrogate model of the system using the Box-Behnken response surface methodology (RSM) is integrated with the particle swarm optimization (PSO). This causes a significant improvement in the effectiveness and efficiency of optimization. The results acquired from RSM are verified by their simulation using the finite-element method, and the accuracy of RSM is proved. Finally, the deterministic and robust optimized motors are simulated in mass production using Monte Carlo analysis, and six-sigma quality achievements are demonstrated.

This paper proposes a novel deep learning (DL) based inter-turn short circuit fault (ISCF) severity diagnosis method using the transformer neural network (TNN). The input features are the currents in alpha-beta reference frame, while the outputs are the number of shorted turns and short circuit (SC) current amplitude. By only monitoring the stator currents, this method provides a comprehensive overview of the fault severity. The proposed TNN creates multiple representations of the input using the multi-head attention (MHA) mechanism. This allows the network to focus on specific parts of the input signals and create an accurate estimate. The dataset was collected using a motor with re-wound windings to simulate stator ISCF, which was operated under nine specific load and speed conditions and three numbers of shorted turns. Both the shorted turns and SC current amplitude estimations on the test dataset have higher than 96% accuracy. A comparison of several methods is presented based on various criteria. Based on the comparison and considering the achieved accuracy, the proposed method shows high potential in terms of comprehensiveness, accuracy, practicability, and cost.

Synchronous generator (SG) plays a vital and critical role in the power system by supplying electric power to consumers. Various faults in SGs can cause some catastrophic events such as power disruption or blackout. These faults can be classified into two electrical and mechanical faults. Short circuit in stator windings and field winding are electrical fault while bearing, static/dynamic eccentricity, and broken damper bars faults are mechanical one. Unlike the induction machines, there are no much researches in SGs condition monitoring owing to its complex behaviour against the faults. Herein, the SG modelling approaches are presented briefly to elaborate shortcoming and challenging issues in the modelling, and then a comprehensive review of various electrical and mechanical fault detection methods is presented.