Iet Electric Power Applications最新文献

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.

To mitigate the potential loss of computational accuracy in the Reduced-Order Model (ROM) due to modal changes during transformer operation, this paper proposes a dynamic updating method for the ROM. This method enables the model to dynamically adjust and adapt to system changes. When transformer operating conditions change, new snapshot data is employed to update the original snapshot matrix, while the POD modes are updated by integrating matrix low-rank decomposition with the Singular Value Decomposition (SVD) results of the original snapshot matrix—thus avoiding the need of SVD for the new snapshot matrix. By incorporating discrete measurement data from the winding temperature, the modal coefficients are solved in real-time based on Gappy POD, facilitating the construction of the dynamic ROM. The proposed method was validated using a simulation model of 110 kV transformer windings. The results demonstrates that the maximum error in updating the POD modes is only 3.60 × 10⁻⁶, with a single update requiring approximately 0.12s. Furthermore, the dynamic ROM reduces the maximum error by 1.78 K. Without considering the snapshot matrix formation time, the average computation time for each time step is about 0.02s. This study presents a novel solution for the dynamic application of the ROM in the transformer temperature field.

Reliable fault diagnosis of power transformers is vital for ensuring the safe and continuous operation of power systems. Although deep learning methods have shown success with single-sensor data, their diagnostic performance remains limited due to the inability to capture complex, multimodal fault characteristics. To address this, we propose an attention-guided semi-supervised vibration-acoustic fusion (AG-SVAF) model, which combines vibration and acoustic signals to enhance diagnostic robustness under limited labelled data conditions. The model integrates time-frequency representations derived via short-time Fourier transform (STFT) with a multilevel attention mechanism—including channel, spatial and self-attention—to highlight fault-relevant features and model cross-modal dependencies. A novel attention-weighted consistency loss further improves the utilisation of unlabelled data during training. Validated on practical transformer datasets, AG-SVAF achieves superior performance in terms of diagnostic accuracy and stability, particularly under challenging scenarios involving class imbalance and label scarcity. This approach provides a promising and scalable solution for intelligent condition monitoring in real-world power system applications.

One important usage of electric motors is railway transportation. There are different types of motor analyses available in railway transportation such as electromagnetic, thermal and mechanical analyses. The goals of thermal analysis are to calculate and monitor motor temperature in components, avoid motor damage, insulation break and increase lifetime. Methods of thermal analysis include: lumped parameters (LP), finite element (FEA) and computational fluid dynamics (CFD). Good analysis should be suitable for the motor type and geometry, obtain good accuracy and reduce time consumption. In this paper, the authors perform the thermal analysis of a self-ventilated 200 kW induction traction motor used in urban trains. First, the thermal network is created and the proposed LP approach is provided to calculate temperatures. This model consists of two steps which makes calculation easier, takes less time and can be used for complex parts without changing the whole model. The temperatures of important parts such as end winding can be calculated by the proposed model with simple equations, without any change in motor parameters matrix, so the proposed model is more flexible and can be used for different cases. Then, FEA and CFD are used to simulate the motor. Some model parameters are optimised using equations to make the model more accurate. Finally, simulation results are compared with experimental test results in order to validate motor performance and the proposed model results.

With the consideration of the DC-link voltage fluctuation, the motor output torque contains a large number of fluctuating components, which may trigger electromechanical coupling resonance (EMCR) and deteriorate the system control performance. In order to solve this problem, the model of the electromechanical coupled system is established, and the mechanism of EMCR generation is analysed. On this basis, a method to suppress EMCR by destroying the EMCR generation conditions is proposed. The method is implemented by stator voltage harmonic compensation and speed harmonic feedback. In addition, the control performance of the system with the proposed suppression method is analysed under different conditions. Finally, extensive tests are performed to verify the effectiveness of the proposed method.