Iet Electric Power Applications最新文献

The advanced sensorless technology of the permanent magnet synchronous machine (PMSM) can replace the position sensor on the machine side, however, the encoder is still the necessary feedback on the load side for a full-closed-loop servo system. This article proposes a sensorless drive scheme assisted by the computer vision to further replace the load side encoders, so to form a quasi-sensorless servo system. Although the vision technology can easily provide the load position for the closed-loop motion control, the feedback continuity cannot be satisfied especially for the multi-object scenarios, where the vision capture device and the computation ability cannot provide an ideal image processing. The issue of spatial-temporal discontinuity is discussed and the solution on basis of fusing the electrical and kinetic models is proposed. With this idea, the only feedback of the full-closed-loop servo drive is the computer vision, the motor side and load side encoders are both cancelled, and the reliable control performance with respect to the heavy load and high precision positioning is maintained. The scheme is validated on a heavy-load full-closed-loop servo bench driven by a sensorless PMSM with only feedback captured from a regular camera.

By incorporating the advantages of magnetic gearing effect and claw pole structure, a new type of claw-pole electric-excitation field-modulation (CPEEFM) machine has been developed, which has potential direct-drive application prospects due to its flexible field regulation ability and high rotor reliability. However, the axial asymmetry and the flux distribution complexity make it difficult to analytically calculate the iron loss. The purpose of this paper is to propose an improved iron loss calculation model for this CPEEFM machine, in which the iron loss coefficients are corrected accordingly by introducing the three-dimensional (3-D) flux density distortion rate to fully consider the influence of axial flux distribution in addition to the radial and tangential flux distributions. Taking a 24-slot/14-pole CPEEFM machine as an example, its iron loss characteristics under different operation conditions are calculated by the proposed model and compared with the traditional Bertotti model and the 3-D finite element analysis (FEA) implemented by JMAG software based on Fast Fourier Transform (FFT) model, which shows an acceptable consistency and improved accuracy. Furthermore, a prototype is finally fabricated and the experimental testing is carried out to verify the validity of the proposed iron loss analysis model.

The solid-state transformer (SST) is an emerging technology that offers promising benefits for electricity distribution and transmission systems, as well as its connection with sources of renewable energy (RE), battery storage, and electric vehicles. The high-frequency magnetic link (HFML)-based SST has enormous promise for solving problems associated with connecting the grid with RE and nonlinear loads and providing extra control functionality. Conventional modular SST topology with multiple active bridge (MAB) converters considers individual multi-winding HFML (MWHFML) in each phase. These systems have the drawbacks of power mismatch between the windings of MWHFML, system complexity for extended modular design, and system cost. This paper proposes a novel SST topology using a three-phase high-frequency magnetic link (TPHFML). Instead of using individual HFML in each phase of a three-phase system, only one TPHFML is used to simplify the entire SST topology. The proposed system reduces the power mismatch issues in the HFML, simplifies the system configuration, reduces 50% of components, and improves the overall efficiency. The proposed TPHFML-based SST system can offer approximately 11.45% higher efficiency than that of the existing MWHFML-based SST system. Experimental tests were carried out successfully to validate the scaled-down prototype of the proposed TPHFML-based SST topology.

This paper addresses the problem of traditional search coils being unable to detect the rotor position of permanent magnet synchronous motors (PMSMs) in low-speed regions. A rotor position detection method based on the mutual inductance effect of search coils is proposed. By designing the structure of the search coils, the mutual inductance effect of the armature winding on the search coils and the influence of the coil's back electromotive force on rotor position detection is eliminated. An ultrahigh-frequency sinusoidal voltage signal with a frequency of 100 kHz is injected into the search coils. The rotor position is calculated by extracting the effective value of the mutual inductance voltage, achieving rotor position detection across the full speed range of the motor. An experimental prototype containing search coils was fabricated, and the experimental results verified the correctness of the theoretical analysis and the effectiveness of the proposed method.

High-temperature superconducting (HTS) materials show potential advantages for direct drive wind turbine generators by enabling higher power density and lower volume. However, incorporating HTS windings necessitates a cryogenic system, which can increase costs, add weight and reduce overall generator efficiency. Consequently, investigating AC losses in HTS windings is crucial given the losses largely influence the cryogenic requirements. C-GEN is a permanent magnet synchronous generator conceived by the University of Edinburgh, which is characterised by modular structures for easy installation and maintenance. In this work, we have numerically investigated the AC losses in a MW-class direct drive air-cored HTS wind turbine generator, which has evolved from the C-GEN topology. Finite-element method (FEM) modelling was applied using COMSOL Multiphysics to build generator models equipped with HTS windings. Four designs were analysed in this study. Design-1 follows a conventional C-GEN structure and serves as a reference, utilising permanent magnets (PMs) for the rotor and air-cored copper windings for the stator. Designs-2 and 3 incorporate partial HTS structures. Design-2 replaces the stator copper coils with HTS windings, whereas Design-3 substitutes the rotor permanent magnets with closed magnetic loop (CML) HTS coil arrays. Design-4 features a fully HTS design, with both HTS rotor and stator windings. Dynamic analysis of the AC losses in HTS windings was conducted. The simulation results show that both partially and fully HTS designs provide higher power density compared with conventional design.

The application of cryogenic and superconducting technologies could revolutionise aircraft electric propulsion systems by substantially increasing their power density and energy efficiency. However, designing and selecting components that can operate at cryogenic temperatures present significant challenges. Fault protection in the closely coupled onboard DC power networks of electric aircraft is particularly difficult due to the rapid rise and high magnitude of fault currents. A hybrid DC circuit breaker integrated with a current limiter is a promising solution for fault protection. In this paper, a high-power 3.3 kV/1.5 kA press-pack IGBT has been chosen and investigated as the main breaker of the proposed hybrid DC circuit breaker, with metal oxide varistors (MOVs) to clamp the voltage during current interruption. Modifications to the press-pack IGBT and MOV are introduced to make them compatible for cryogenic operations. The junction temperature rise of the press-pack IGBT during current interruption is then simulated. High-current interruption tests are conducted at both room temperature and cryogenic temperatures. The experimental results demonstrate that the 3.3 kV/1.5 kA press-pack IGBT can interrupt currents exceeding 5.1 kA when immersed in a liquid nitrogen bath. Additionally, the voltage across the DC circuit breaker is clamped below 1 kV using cryogenically compatible MOVs.

A highly efficient testing method (oscillating wave method) completes several transformer tests via one connection and presents high sensitivity to winding faults. Existing methods extract independent features directly from the detection signals and merely combine them without considering the correlation and complementarity between these features. This oversight leads to insufficient characterisation of fault information, thereby reducing diagnostic accuracy. Therefore, a high-correlation feature screening and combination method is proposed for processing oscillating wave signals. First, a multidimensional transformation method for oscillating wave signals is developed. Secondly, the time–domain signals acquired through the oscillating wave method are transformed into a time–frequency domain image, from which colour and texture features are extracted. Next, single features from different dimensions are integrated into multiple composite features by employing a feature fusion technique. Then optimal multifeatures are selected using the standardised cluster-centre parameter. Based on the multifeatures, the intelligent algorithm completes the classification. Finally, simulation results demonstrate that the multifeature fusion method, which incorporates colour and texture features, can accurately identify fault types, degrees and locations. This approach offers crucial technical support for the automated analysis of oscillating wave signals.

This paper proposes a multi-torque component decomposition method combining with frozen permeability method, and for the first time, accurately decomposes six torque components of dual-PM (DPM) machines. Based on the decomposition results, the characteristics of torque components are investigated. It is found that for average torque, the components generated by armature field interacting with PM fields and iron cores have positive contributions, while the components generated by the interactions between stator PMs, rotor PMs, and iron cores have negative contributions and are affected by magnetic saturation. The phases of torque ripple of torque components are different, resulting in a low resultant torque ripple of DPM machines. Moreover, for cogging torque, the component generated by the interaction between stator PMs and rotor PMs is the major source, and the components generated by the interactions between PMs and iron poles have a cancelling effect. Finally, a DPM machine is prototyped and tested to verify the analyses.

The energy backflow phenomenon is unavoidable in small dc-link capacitor-based PMSM drive systems, which is first identified and comprehensively analysed in this paper. This phenomenon occurs when the amplitude of the stator voltage exceeds the minimum value of the fluctuating dc-link voltage. To address this issue, flux weakening (FW) control can effectively reduce the stator voltage and mitigate the energy backflow phenomenon. However, dc-link voltage fluctuation can introduce several challenges to the conventional feedback FW control. For instance, a dc offset in the calculated d-axis reference current is identified in this paper due to the dc-link voltage fluctuation. Additionally, an analysis of the small-signal model reveals that when the fluctuating q-axis voltage becomes negative, it can lead to system instability. Moreover, the commonly used PI controller in FW control fails to adequately control the ac component introduced by these fluctuations. Therefore, this paper proposes an optimised FW control method to mitigate the energy backflow issues. Furthermore, an optimal phase angle selection method for the d-axis reference current, based on the least mean square algorithm and gradient descent algorithm, is introduced to suppress current ripple caused by the fluctuating dc-link voltage. Experimental results validate the effectiveness of the proposed optimised methods.

Accurately estimating the magnetization distribution in permanent magnets is critical for optimising their performance in various applications, such as electric motors, generators and magnetic sensors, where precise magnetic field control is essential. A physics-informed neural network (PINN) is demonstrated to solve the inverse problem of magnetization distribution within the volume of permanent magnets. A neural network is constructed to model the spatially dependent magnetization in the magnet. The physical model, based on the Biot–Savart law, is integrated into the PINN framework. The discrepancy between the magnetic field calculated by the physical model and the externally observed one is used to guide the network training, exhibiting both the model-based and data-driven characteristics of the PINN. The accuracy and robustness of the proposed PINN are demonstrated through numerical experiments with both uniform and nonuniform magnetization scenarios, as well as both noise-free and noisy observation data. This study provides a new approach for solving magnetization distribution estimation problems, benefiting the development of high-quality permanent magnets for electrical engineering applications.