Several factors including fossil fuels scarcity, prices volatility, greenhouse gas emissions or current pollution levels in metropolitan areas are forcing the development of greener transportation systems based on more efficient electric and hybrid vehicles. Most of the current hybrid electric vehicles use electric motors containing powerful rare-earth permanent magnets. However, both private companies and estates are aware of possible future shortages, price uncertainty and geographical concentration of some critical rare-earth elements needed to manufacture such magnets. Therefore, there is a growing interest in developing electric motors for vehicular propulsion systems without rare-earth permanent magnets. In this paper this problematic is addressed and the state-of-the-art of the electric motor technologies for vehicular propulsion systems is reviewed, where the features required, design considerations and restrictions are addressed.
Inter-turn winding faults in five-phase ferrite-permanent magnet-assisted synchronous reluctance motors (fPMa-SynRMs) can lead to catastrophic consequences if not detected in a timely manner, since they can quickly progress into more severe short-circuit faults, such as coil-to-coil, phase-to-ground or phase-to-phase faults. This paper analyzes the feasibility of detecting such harmful faults in their early stage, with only one short-circuited turn, since there is a lack of works related to this topic in multi-phase fPMa-SynRMs. Two methods are tested for this purpose, the analysis of the spectral content of the zero-sequence voltage component (ZSVC) and the analysis of the stator current spectra, also known as motor current signature analysis (MCSA), which is a well-known fault diagnosis method. This paper compares the performance and sensitivity of both methods under different operating conditions. It is proven that inter-turn faults can be detected in the early stage, with the ZSVC providing more sensitivity than the MCSA method. It is also proven that the working conditions have little effect on the sensitivity of both methods. To conclude, this paper proposes two inter-turn fault indicators and the threshold values to detect such faults in the early stage, which are calculated from the spectral information of the ZSVC and the line currents.Energies 2019, 12, 2733 2 of 15 ripple in the output, although torque pulsations can be minimized by means of a suitable design, including rotor skewing, asymmetric flux barriers or selected rotor steps [5].Faults in electrical machines could produce loss of system reliability, unscheduled shutdowns [9], important economic losses or even harmful effects to humans. Therefore, there is a growing demand for improved fault diagnosis approaches in electrical machines, in particular for high-performance applications [10]. This strategy ensures the safe and reliable operation of the plant, while greatly reducing unexpected and unscheduled fault occurrences, thus improving system availability and minimizing economic losses and the likelihood of accidents [11].Rotating electric machines are designed with mechanical and electrical symmetry to maximize performance and efficiency and to minimize vibrations. When operating under faulty conditions this symmetry is lost, thus changing the magnitude or the shape of different machine signals, such as the electromotive force, line currents, vibrations profile or the temperature, among others [12]. Most of these faults generate specific patterns of such signals, so these changes can be used for fault diagnosis purposes.Different fault diagnosis techniques have been analyzed in the technical literature, based on on-line or off-line approaches. Whereas off-line methods require the disconnection of the machine and sometimes the removal of some components, on-line diagnosis methods require the addition of specific sensors to acquire data from the machine during normal operation, although in some cases no extra sensors are required. Due to the ex...
Air gap eccentricity faults in five-phase ferrite-assisted synchronous reluctance motors (fPMa-SynRMs) tend to distort the magnetic flux in the air gap, which in turn affects the spectral content of both the stator currents and the ZSVC (zero-sequence voltage component). However, there is a lack of research dealing with the topic of fault diagnosis in multi-phase PMa-SynRMs, and in particular, those focused on detecting eccentricity faults. An analysis of the spectral components of the line currents and the ZSVC allows the development of fault diagnosis algorithms to detect eccentricity faults. The effect of the operating conditions is also analyzed, since this paper shows that it has a non-negligible impact on the effectivity and sensitivity of the diagnosis based on an analysis of the stator currents and the ZSVC. To this end, different operating conditions are analyzed. The paper also evaluates the influence of the operating conditions on the harmonic content of the line currents and the ZSVC, and determines the most suitable operating conditions to enhance the sensitivity of the analyzed methods. Finally, fault indicators employed to detect eccentricity faults, which are based on the spectral content of the stator currents and the ZSVC, are derived and their performance is assessed. The approach presented in this work may be useful for developing fault diagnosis strategies based on the acquisition and subsequent analysis and interpretation of the spectral content of the line currents and the ZSVC.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.