Abstracts:This paper proposes a new type of axial brushless synchronous generator that allows controlling the excitation field. No additional generator or permanent magnets are needed. The new generator has both the field winding and the armature in the stator. Therefore, the ability to adjust the excitation current is achieved without brushes or slip rings. Numerical (FEM) analysis and experimental results confirm that the new structure is realizable. Due to its unique construction features, the new generator has high efficiency and power density.

Abstracts:This article proposes a hybrid analytical model for detent torque analysis of multi-stator-module permanent magnet (PM) arc machine (MSM-PMAM) considering the asymmetric stator and rotor eccentricity. The detent torque equation-based model is used to derive a general design principle of MSM-PMAM with reduced detent torque, while the harmonic magnetic field-based model is used to reveal the generation mechanism of detent torque. The cogging torque and end torque are derived, and a general design method for MSM-PMAM to reduce the detent torque is proposed, which is verified through finite element analysis (FEA). Then, a unified model for both stator-PM and rotor-PM machine is established to reveal the generation mechanism of detent torque considering harmonic magnetic fields for circular machines. The detent torque characteristics of MSM-PMAM are derived by introducing a “equivalent slot” concept and the influence caused by asymmetric stator modules and rotor eccentricity is also analyzed. A three-stator-module PMAM is designed and the detent torque is analyzed based on an improved Schwarz-Christoffel mapping method. The experimental and FEA results validate the correctness of the proposed method and analysis.

Abstracts:Insulation degradation monitoring is crucial for the reliability of induction motors. Current signal-based methods are non-invasive and easy to acquire. However, the insulation features in current signals are weak, easily affected by interference, and may appear similar under different insulation conditions, leading to potential misdiagnosis and making the task challenging. To address this, this article first establishes a common-mode impedance model of the stator winding to explore the relationship between insulation degradation and current. On this basis, a data-driven new method for the insulation monitoring of induction motors (IM) is proposed. Firstly, high-pass filtering is used to remove low-frequency interference and retain the high-frequency current components containing key insulation information. Then, the proposed dual-spectrum aggregation module (DSAM) integrates the amplitude and phase spectra of the current data, and adopts the squeeze-and-excitation (SE) mechanism to enhance the model's attention to critical features. Subsequently, the enhanced signal is input into the modified ConvNeXt network for insulation condition recognition. Experimental results demonstrate that the proposed method achieves outstanding monitoring accuracy with low computational complexity under various operating conditions, providing an effective health management solution for induction motor systems.

Abstracts:A novel rotor structure for magnet-assisted wound field motors was proposed in this paper. In the pervious research, to improve the torque density of the magnet-assisted wound field motor, a field-unit-type rotor was proposed. This study showed that reluctance torque can be utilized in wound-field motors, contributing to improved torque density. However, when the same structure is applied to hybrid and electric vehicles, the strength characteristics on a high rotational speed was an issue. Therefore, this study proposed a novel rotor structure using a split rotor core and a distributed mortise structure. This structure improved the strength characteristics while maintaining the advantages of the field unit structure proposed in the previous study. To validate its effectiveness, the magnetic and mechanical properties of the proposed motor were verified using numerical and experimental verification, and the obtained results were compared with those a of conventional motor. The results confirmed that the proposed structure was effective in improving the performance of magnet-assisted wound field motors. In addition, the proposed motor was compared with an existing IPM motor of hybrid vehicle, and showed comparable mechanical output and high-efficiency in the high speed range.

Abstracts:This paper proposes a novel terminal sliding mode control strategy for the speed control of permanent magnet synchronous generator (PMSG)-based wind energy conversion systems (WECSs) to improve wind power generation efficiency. Firstly, a novel terminal sliding mode function based on exponential convergence is constructed, and a controller is designed accordingly, effectively addressing the system's nonlinear characteristics while achieving high precision tracking and chattering suppression with rapid convergence. Secondly, an unknown system dynamic estimator (USDE) is designed to estimate disturbances caused by external environments and inaccurate model parameters, further enhancing the system's disturbance rejection capability. Finally, the proposed USDE-based novel exponential sliding mode control is applied in a 1000 W PMSG-based WECS hardware simulator. Experimental results indicate that compared to the full-order sliding mode controller, the proposed control method exhibits stronger speed tracking, chattering suppression, and disturbance rejection capabilities.

Abstracts:Reliable mechanical fault detection with limited data is crucial for the effective operation of induction machines, particularly given the real-world challenges present in industrial datasets, such as significant imbalances between healthy and faulty samples and the scarcity of data representing faulty conditions. This research introduces an innovative meta-learning approach to address these issues, focusing on mechanical fault detection in induction motors across diverse operating conditions while mitigating the adverse effects of drive noise in scenarios with limited data. The process of identifying faults under varying operating conditions is framed as a few-shot classification challenge and approached through a model-agnostic meta-learning strategy. Specifically, this approach begins with training a meta-learner across multiple interconnected fault-diagnosis tasks conducted under different operating conditions. In this stage, cross-entropy is utilized to optimize parameters and develop a robust representation of the tasks. Subsequently, the parameters of the meta-learner are fine-tuned for new tasks, enabling rapid adaptation using only a small number of samples. This method achieves excellent accuracy in fault detection across various conditions, even when data availability is restricted. The findings indicate that the proposed model outperforms other sophisticated techniques, providing enhanced generalization and quicker adaptation. The accuracy of fault diagnosis reaches a minimum of 99%, underscoring the model’s effectiveness for reliable fault identification.

Abstracts:Electrodynamic suspension (EDS) technology has broad application prospects in high-speed rail transit systems. Accurate and efficient electromagnetic force calculations are essential for system design and optimization. This paper derives analytical expressions for transient forces in a null-flux superconducting EDS train based on the harmonic approximation of mutual inductance. Neumann’s formula, combined with a cross-sectional discretization technique, is employed to accurately compute the dynamic mutual inductance between the onboard superconducting magnets and the ground suspension coils. Induced current in the coils is derived from dynamic circuit theory, and the transient forces on the magnets are obtained via the virtual displacement method. An equivalent impedance is introduced to account for electromagnetic coupling among the suspension coils. This model is validated against experimental data, with relative errors of less than 3% . A comparative analysis with both finite element and widely used analytical models demonstrates that our model yields force calculation error below 2.4%, while reducing computation time by 43% compared to the most popular analytical model. The influence of mutual inductance harmonics on transient forces is analyzed. The results indicate that over 98% of the average suspension force is determined by the fundamental mutual inductance, while the force harmonics are even multiples of the magnet-to-coil length ratio, originating from the 3rd, 5th, and 11th harmonics of the mutual inductance. This study establishes a theoretical foundation and offers methodological guidance for optimizing the electromagnetic performance of EDS trains.

Abstracts:To simultaneously suppress the periodic and aperiodic disturbances experienced by permanent magnet synchronous motors in model predictive control, a model-free predictive current control with adaptive resonance control based on an ultra-local model is proposed. Firstly, the ultra-local models of the speed loop and the current loop are established respectively, and the unmodeled and uncertain unknown part F in the ultra-local model is estimated by using a high-order fast terminal sliding mode observer, and its stability is analyzed. Then, an adaptive resonant controller (ARC) is designed to suppress the periodic disturbance caused by the dead zone effect. The ARC and the model-free control based on the ultra-local model are combined to compensate for their respective shortcomings, achieving the suppression of periodic and aperiodic disturbances in the system. Finally, by comparing with the traditional duty cycle model predictive control and the model-free model predictive control without ARC, the proposed scheme is proved to be robust to periodic and aperiodic disturbances.

Abstracts:The acoustic profile of electrical vehicles is one of the most important parameters for consumers, when they select a car to purchase, considering models with similar characteristics. Therefore, even minor acoustic pollution produced by one of the auxiliary car systems, may worsen the customer's impression and significantly impact their choice. As a result, car manufacturers pay considerable attention to noise characteristics of all vehicular systems and initiate many researches in this area. One of the emerging topics is compressor operation in preheating mode, where compressor oil is heated to decrease its viscosity while the compressor is not rotating. Therefore, even minor operational noises are perfectly recognized and worsen the acoustic profile. In order to decrease these noises, the proposed paper studies compressors, operating in preheating modes, evaluates irradiated acoustic noises and proposes several directions to improving acoustic profiles. Thereafter, all approaches are analyzed, evaluated and the best technique is selected for further work. The feature of this work is the usage of equal loudness contours (ELCs) and equal annoyance contours (EACs), which reflects the specifics of human ear to sense sounds of various frequencies.

Abstracts:This study investigates the common mode current path in voltage source inverter-fed electrical machine systems, focusing on its distribution within stator laminations and the outer frame. Via finite element analysis and experimental validation, it challenges the notion that the common mode current mainly follows a zigzag pathway along the inner surface of stator laminations in inverter-fed machines. The findings reveal that while part of current flows through the laminations, the majority flows through the frame outer surface, influenced by the skin effect. These results underscore the importance of understanding the common mode current pathway for better estimation and mitigation of the shaft voltage and bearing currents in inverter-fed machines.