Iet Electric Power Applications最新文献

High-frequency transformers (HFTs) are critical in modern power electronics, especially for application scenarios requiring compact size, high efficiency and superior thermal stability. However, their performance is often constrained by manufacturing tolerance and material property variations. This paper presents a robust optimisation design (Rob. D) method for nanocrystalline HFTs (nanoHFTs) that considers the material uncertainty, focusing on optimising power density, leakage inductance and thermal stability simultaneously. The support vector regression (SVR) is applied to the surrogate model to replace the computationally expensive finite element analysis (FEA) during the extensive uncertainty evaluations required in MCA, whereas the nondominated sorting genetic algorithm III (NSGA-III) leverages the surrogate model to efficiently navigate the trade-offs between competing objectives under variability. The Pareto-optimal solutions achieve a 1.26°C decrease in hotspot temperature of nanoHFT. The validation via simulations and a 20 kVA prototype confirms that Rob. D reduces the standard deviations of the core uncertainty to 2.7 × 10⁻³°C, substantiating the framework's efficacy in balancing competing objectives under uncertainty. Compared to conventional deterministic optimisation design (Det. D) approaches, the proposed method demonstrates enhanced robustness by treating core dimensions and magnetic properties as statistically distributed variables. This enables optimisation of both mean performance and standard deviation of objectives, ensuring resilience against the actual manufacturing dispersions.

Recently, wireless power transfer (WPT) effectively meets the demands for distance, transfer power level, system efficiency and safety, making it highly promising for various applications. In practical applications, system performance is sensitive to the coil coupling, making reliability against coupling fluctuations a real challenge. This article focuses on the coil modelling for coils for wireless power transfer systems, of which the self-inductance, mutual inductance, B field are all taken into consideration. Besides the accurate modelling, the coil optimization is conducted for better anti-misalignment to achieve a robust stable performance. Finally, an experimental prototype is implemented, and the results validate the accuracy of the proposed model.

The synchronously rotating reference frame (SRRF)-based proportional-integral (PI) control technique has been though studied in many different inverter applications, a well-designed and clearly presented application of this technique for the three-phase, three-level, three-leg and four-wire (3P3L3L4W) grid-connected (GC) neutral point clamped (NPC) inverter has not been found. Therefore, in this study, firstly, the 3P3L3L4W GC NPC inverter is controlled with the SRRF-based PI controller. Then, to achieve an optimal artificial neural network (ANN) controller in terms of computational burden and control performance, two different ANN controllers, named ANN-1 and ANN-3, are designed with data obtained from the PI controller. The control objectives of the NPC inverter are carried out by a single ANN in ANN-1 and by three independent ANNs in ANN-3. The training results for ANN-1 and ANN-3 are approximately the same, but their computational burdens are quite different. Because ANN-3 consists of three ANNs with minimum complexity, it has much less computational burden than ANN-1. Their control performances are compared by using the MATLAB/Simulink, and presented for constant current reference, sudden changes in current reference, current reference with white Gaussian noise, sudden changes in DC source voltage, grid voltage imbalance, sag and swell, and different line filter parameters.

To address the critical challenges in defect detection of basin insulators utilised in power distribution systems, this study proposes a Dilated Weighted-Across Stages Pyramid Network-Transformer (DSPN-Transformer) framework specifically designed for x-ray digital radiography (X-DR) applications. The current methods face issues such as limited robustness to imaging artefacts, high miss rates for subtle defects, and indistinct feature representations in low-contrast regions. The proposed framework leverages a Swin Transformer backbone to model long-range dependencies while enhancing attention to subtle defect boundaries in low-contrast regions. Building upon this foundation, a new Dilated Weighted-Across Stages Pyramid Network (DSPN) is designed to dynamically adjust multi-scale receptive fields and spatial-channel weights, effectively amplifying defect-related features. Additionally, a Dynamically-Aggregated Feature Module (DAFM) is introduced to achieve adaptive channel-wise fusion of hierarchical features, further improving the discrimination of defect patterns. Experiments on the MLDB_IRD dataset demonstrate that the proposed DSPN-Transformer achieves 97.58% accuracy, 97.23% AUC, and 95.57% F1-score. The DSPN-Transformer ensures reliable operation of power systems through intelligent diagnosis of critical grid components.

In this paper, a novel six-phase rotor-permanent magnet axial field modular fault-tolerant flux-switching machine (RPM-AFMFTFSM) is proposed. The separated stator core and rotor cells provide effective electromagnetic isolation for the armature windings, and this leads to enhanced fault-tolerant operating capability. The segmented permanent magnet (PM) is integrated in the rotor, by which the magnetic saturation of the stator iron core is alleviated and the PM eddy current loss is reduced. The stator-slots and rotor pole-pairs (P_s/P_r) combination of the proposed machine is optimised, and the cogging torque is reduced. A comparative study between the RPM-AFMFTFSM and conventional stator permanent magnet axial field flux-switching machine (SPM-AFFSM) is carried out by 3-D finite element analysis (FEA) method. The advantage of the proposed machine with respect to the overload capability, flux-weakening capacity and antidemagnetisation ability are revealed. The fault-tolerant performance under single-phase and two-phase open-circuited conditions is analysed. The full-bridge inverters are employed on the six-phase armature windings to achieve the reduced amplitude of the fault-tolerant current and copper loss by adopting the round rotating magnetomotive force reconfiguration control strategy. Finally, a prototype of the RPM-AFMFTFSM is manufactured and the FEA predicted results are validated by experimental measurements.

In order to improve the power density and efficiency of a hydrogen fuel vehicle, it is an effective method to design an ultra-high-speed centrifugal air compressor to supercharge the incoming air, which is also a great challenge. In this paper, four types of ultra-high-speed permanent magnet motors with integrated rotors are proposed based on permanent magnet materials and structures, and the maximum speed of the motors reaches 100 krpm. The integrated rotor considering interference assembly is designed and analysed by the finite element method. Furthermore, the integrated rotor was analysed in terms of electromagnetism, temperature, stress and dynamics, and the reliability of the rotor with different structures and materials was investigated. Finally, a 25 kW ultra-high speed permanent magnet motor is manufactured and tested to verify the effectiveness of the design, which provides guidance for the design and manufacture of ultra-high speed permanent magnet motor.

The requirement for high speed reliable and efficient bearing operation drove the research into magnetic bearings. In the past decades, active magnetic bearings (AMBs), which are known for their operational cost and complexity, have received significant attention, and have been utilised in high-speed industrial applications. On the other hand, electrodynamic magnetic bearings (EDBs) are a different type of magnetic bearing that offers passive, efficient and more importantly stable operation. Nevertheless, despite these advantages, EDBs have received relatively little attention. This paper focuses on the heteropolar variant of EDB, with particular emphasis on winding configurations and the effects of design parameters. A comparison between winding configurations recommended in the literature and a simpler winding configuration proposed by the authors is undertaken. It is shown that both winding configurations exhibit similar performance, in terms of restoring force production and stability. Furthermore, the effects of the leading design parameters of EDB employing the proposed windings are investigated. Moreover, to validate some of the findings a test rig is developed, and good agreement between measured and predicted forces is shown for different eccentricities.

Xu, H., Zhu, Z.Q., Yang, L., Chen, L. and Zhou, Y. (2025), Decomposition and Investigation of Torque Components of Dual-PM Machines. IET Electr. Power Appl, 19: e70024. https://doi.org/10.1049/elp2.70024.

The conflict of interest statement in the originally published version was incorrect. The correct conflict of interest statement is given below:

The Editor-in-Chief was not involved in the handling of the article or its peer review process. The Deputy Editor-in-Chief and handling Associate Editor have taken full responsibility for the editorial process for the article. The authors declare no further conflicts of interest.

We apologise for this error.

High torque output capability is crucial for electric vehicle in-wheel machines. In order to comprehensively evaluate the performance of different out rotor flux reversal permanent magnet (FRPM) machines, a comparative study of two FRPM machines with different topologies is conducted. Different from the conventional consequent pole FRPM machines (CPFRPM), the FRPM machine with auxiliary teeth (ATFRPM) features a stator tooth with a complete piece of permanent magnet (PM) of the same polarity, whereas the adjacent stator tooth is devoid of any PM. The paper introduces the topology structure of the machine using a 24-slot/22-pole combination and analyses its operation principle. Geometrical parameters are globally optimised to improve torque performance of FRPM machines. Furthermore, the electromagnetic characteristics of the CPFRPM and ATFRPM machines are compared under the same current density. The ATFRPM machine exhibits superior performance in efficiency, power factor and torque density. Finally, an ATFRPM machine prototype is manufactured to verify the theoretical analysis, and the experimental results confirm the effectiveness of the simulation analysis and optimised design.

This study addresses the challenges of machine learning-based condition monitoring of induction machines under varying load conditions, which can result in low accuracy at unmeasured loading levels. A hybrid data augmentation framework is developed that combines multiple regression models and ensemble learning techniques to generate feature values at any unmeasured loading levels. The proposed method requires feature computation from only four measured loading levels under healthy, one, two and three broken rotor bars conditions as training data, enabling feature values augmentation for other loading levels. In this study, the augmentation method is applied to generate feature values at two intermediate levels (50% and 75%) and one extreme level (100%) and the corresponding results are presented. This hybrid data augmentation method not only produces accurate feature values for intermediate loading levels but also performs exceptionally well in extrapolating feature values at extreme loading levels. Incorporating this generated data during the training phase resolves generalisation issues and substantially improves the classification accuracy of machine learning models. In particular, the integration of ensemble learning techniques helped to increase accuracy from 38.75%, 42.75% and 60%–100% for the K-nearest neighbours, support vector machine and decision tree models, respectively, at the 100% loading level.