Iet Electric Power Applications最新文献

This study proposes a modified partitioned-stator flux-switching permanent magnet (PS-FSPM) machine and reduces the permanent magnet usage in conventional PS-FSPM machines. To achieve this goal, the stators of the conventional PS-FSPM with outer-armature/inner-PM (OA/IPM PS-FSPM) structure are swapped to realise a PS-FSPM with outer-PM/inner-armature (OPM/IA PS-FSPM) design. The machine topology along with the operating principles are described in detail, and an analytical airgap permeance model is introduced for the proposed machine. The number of rotor modules, the widths of the magnets and rotor modules, as well as the split ratio (SR), are optimised through sensitivity analysis to achieve higher torque density and reduced torque ripple, compared with the conventional structure. Moreover, a flux barrier in the exterior surface of the outer stator is adopted to improve the proposed structure to reach a higher flux concentration in the airgap and reduce iron volume consumption. The thermal analysis results using the computational fluid dynamics modelling indicate that the temperatures of both insulation and permanent magnets remain within specified operating limits. Finally, apart from the finite element analysis (FEA), an experimental study is performed to evaluate the feasibility of fabricating a machine equipped with a high number of rotor modular teeth.

This paper proposes a novel asymmetric permanent magnet assisted synchronous reluctance motor (APMA-SynRM) used for electric vehicles (EVs), which adopts the magnetic isolation flux barrier to enhance the effect of magnetic field shifting. Firstly, the magnetic field shifting principle and magnetic circuit model of APMA-SynRM are analysed. Then, a new design method of APMA-SynRM that makes the reluctance torque and permanent magnet (PM) torque reach the peak value at same current angle is proposed. According to the design method's analysis, the condition that the structural and magnetic property parameters of APMA-SynRM should meet is obtained, which provides significant reference for the APMA-SynRM's design. After that, an APMA-SynRM scheme is designed to verify the design method's accuracy and effectiveness. Finally, the electromagnetic and mechanical strength performances of APMA-SynRM and corresponding permanent magnet assisted synchronous reluctance motor (PMA-SynRM) are studied, which demonstrates the feasibility of APMA-SynRM's application in EVs.

Fault diagnosis of high voltage circuit breaker is an important aspect of electrical equipment intelligence. To effectively identify unknown faults, this paper proposes a high-voltage circuit breaker fault diagnosis method based on open set fusion model (OSFM). Firstly, the current data and vibration data are processed using sequential variational mode decomposition and Fourier transform, respectively, to extract data features, thereby constructing the original feature set of the current-vibration signal, which is then input into the Transformer model for further feature extraction. Secondly, the open-set discriminant model based on the extreme value theory is proposed, and the data output by transformer is input into classifier to realise open-set fault diagnosis. Finally, the tree-structured parzen estimator is used to optimise the selection of transformer model parameters and discriminator acceptance probability. The efficacy of the OSFM was evaluated through experimentation on experimental platform. The results demonstrated that the OSFM method can effectively recognise previously unidentified class faults while maintaining accurate recognition of known classes. Compared with other open-set classification techniques, OSFM can improve the recognition accuracy by up to 38.36%.

This paper proposes a novel axial-radial flux hybrid excitation permanent magnet synchronous motor (ARFHE-PMSM), which has better space utilisation and torque density than traditional radial flux PMSM (RFPMM) and axial flux PMSM (AFPMSM). Due to the complex mechanical structure of ARFHE-PMSM, the computation burden is higher and larger than RFPMM and AFPMSM. Therefore, the sequential Taguchi optimisation method is proposed to improve the performance of ARFHE-PMSM and reduce computation burden. The torque, torque ripple, core loss and permanent magnet volume of ARFHE-PMSM are optimisation targets. In the optimisation process, the design parameters are divided into non-sensitive and sensitive parameters based on the sensitivity analysis results. The non-sensitive parameters are first optimised through the finite element model and then the sensitive parameters are optimised. The motor's manufacturing errors are considered in the optimisation process, and the Gaussian membership function and characteristic index are utilised to transform the multi-objective optimisation problem into a single-objective optimisation problem. The iterative Taguchi method narrows the optimisation scope until the convergence conditions are met. The optimal combination of different design parameters of the motor is obtained by analysing the experimental results. Compared with the initial design, the electromagnetic performance and robustness of the ARFHE-PMSM have been significantly increased.

This paper proposes a model predictive control strategy for induction motors driven by three-level inverters, enabling effective switching frequency adjustment. First, a three-dimensional satisfaction space optimisation strategy is proposed, eliminating the need for weight coefficient adjustments through self-constraints and mutual constraints of the optimisation variables. The optimal switching state is selected by comparing the maximum average dwell time within the satisfaction space, thus reducing the inverter switching frequency. Second, switching frequency is treated as an auxiliary optimisation variable, and a dynamic sliding window method is designed to efficiently track switching frequency in variable-speed systems. The boundaries of the three-dimensional satisfaction space are adjusted to regulate the switching frequency. Finally, experimental results demonstrate that the proposed strategy maintains the switching frequency of 300 Hz across the entire speed range, achieving excellent dynamic and steady-state performance at this low switching frequency.

The multi-objective optimal design of double-sided stator dual-rotor synchronous reluctance machines (DSS-DRSynRMs) is a challenging high-dimensional problem. The objective of this paper is to present a new optimal design method based on data-driven models and the principle of torque decomposition addressing the aforementioned issue. For this purpose, a 26-parameter optimisation problem is solved by employing the proposed method consisting of three sequential phases. Through the proposed method, the combination of artificial neural network (ANN) and recently introduced waveform targeting surrogate model (WTSM) strategy is investigated to mitigate the computational complexity of the optimisation process. Furthermore, the electromagnetic performance of the final optimal design has been comprehensively analysed showing a significant reduction in torque ripple rate and improved torque density. Moreover, the computational efficiency of the proposed method has been compared to the popular multi-level multi-objective optimisation method. From the discussion, it can be found that the proposed method provides a reduced computation time and wider search space.

Permanent magnet synchronous motors (PMSMs) experience considerable performance degradation due to the rise in temperature and the resulting partial demagnetisation in the PMs, as well as the shortenings in the insulations' lifetime. To mitigate the temperature of motor components, it is crucial to investigate and continually improve the design of efficient cooling systems. This study implements the water immersion cooling (WIC) concept on a surface-mounted PMSM (SMPMSM), where through comparing its cooling performance with the forced ventilation cooling (FVC), it is indicated that, even at high inlet velocities for the latter, it cannot maintain the temperature below the specified thresholds and the required input electric power to run the ventilation fan will be increased exponentially to compensate for its ineffectiveness. While in the WIC configuration, the winding and PM temperature values remain well below the margins when the heat transfer coefficient of this method is 40$��\%�$ higher than the FVC. By incorporating the effect of the cooling process through the heat transfer coefficient, the lumped-parameter thermal network (LPTN) is utilised to study the operation mode of the motor under the mentioned cooling configurations. Besides achieving higher cooling efficiency, the WIC strategy can quickly reduce temperature, which is reflected in the thermal time constant of the cooling method extracted from the LPTN. Consequently, it is demonstrated that up to 35$��\%�$ higher than the nominal generated heat, the SMPMSM under the WIC can operate continuously, while for the FVC, the frequent start-stop driving scenario should be employed.

Accurate and efficient calculation of a transformer's magnetic field is fundamental for the rapid calculation of its losses, temperature rise, and structural forces. However, existing numerical methods for calculating the harmonic magnetic field of a product-level transformer are time-consuming and fail to meet the rapid requirements of digital operations and maintenance. To address this, this paper first utilises the harmonic field method to obtain the snapshot matrix of the transformer's magnetic field. Subsequently, a response surface model of the magnetic field is constructed using intrinsic quadrature theory and radial basis functions in the augmented form. To enhance the efficiency of constructing the reduced-order model, an adaptive Latin hypercube sampling method, integrating the additive rule and leave-one-out cross-validation, is introduced, significantly improving the efficiency of sample space construction. The effectiveness of the proposed method is validated by applying the proper orthogonal decomposition-radial basis function including linear polynomial (POD-RBFLP) method to calculate the harmonic magnetic field of a three-phase power transformer in reduced order. The results are compared with those from COMSOL calculations, showing that the reduced-order model maintains the calculation error within a reasonable range, thereby confirming the accuracy of the proposed method. Additionally, the reduced-order model demonstrates a significant advantage in computation time compared to COMSOL simulations, enabling the calculation of the transformer's magnetic field in seconds.

Due to their high power density and superior drive performance, permanent magnet synchronous motors (PMSM) are extensively utilised in the mobile machine traction systems. Traditional id = 0 control produces relatively low torque per unit of current and is suitable for non-flux-weakening control. Traditional maximum torque per ampere (MTPA) control can enhance the torque output capability per unit of current, and is mainly used for interior PMSMs (IPMSMs) with salient poles. However, neither id = 0 control nor MTPA control fully considers the impact of copper losses, iron losses, and variations in motor parameters, resulting in suboptimal control efficiency of the PMSM. In this paper, to further enhance the energy efficiency of IPMSMs, models for iron loss and copper loss in IPMSMs are constructed and analysed. Compared to existing minimum loss optimisation algorithms, a minimum loss angle vector control method based on the loss models is proposed. The particle swarm optimisation algorithm is employed to achieve globally optimal d-q axis current distribution. In addition, considering the influence of parameter variations during the operation of IPMSMs on the efficiency improvement control, online parameter identification of the motor is investigated. Consider the impact of parameter variations on motor performance when using traditional proportional integral (PI) control, a neural network is employed for online tuning of the speed loop of IPMSMs. Experimental research is conducted to verify the effectiveness of the proposed control algorithm. The experimental results demonstrate that the proposed online tuning control algorithm exhibits superior control performance compared to the traditional PI control. Specifically, during the startup phase, the overshoot is reduced by 61.54%, and the adjust time is decreased by 33%. When load variations cause changes in rotational speed, the overshoot is red uced by 33%–60%, and the adjust time is shortened by 34%–50%. Furthermore, the minimum loss angle control can improve energy efficiency by more than 20% compared to id = 0 control, and by 6%–10% compared to MTPA control.

This paper proposes a multi-level optimisation design strategy aimed at enhancing the fault-tolerant capability of a Yoke-less Axial-Field Flux-Switching Permanent Magnet (YASA-AFFSPM) motor. The research addresses the crucial requirement for robustness and reliability in electric motor systems utilised in safety-critical applications. By employing a comprehensive multi-objective optimisation approach, the motor's design is refined at three levels based on the main effect stratification of design variables for the YASA-AFFSPM motor. The design variables are divided into three levels and optimised, resulting in the attainment of a comprehensive optimal solution that considers multiple design goals, including average torque, cogging torque, and fault-tolerant capability. The optimal design places a primary emphasis on attaining a substantial level of fault tolerance for both open- and short-circuit faults. This is accomplished by reducing the ratio of mutual-inductance to self-inductance. To streamline optimisation, the Kriging model approximates finite element analysis at each level. Furthermore, the effectiveness of the design is showcased through the utilisation of a dual-channel winding configuration. A prototype is constructed, and experimental validation demonstrates a significant improvement in fault-tolerance performance, with results indicating enhancements of 14% in average torque and 41% in cogging torque.