Iet Electric Power Applications最新文献

To accurately analyse the deformation of transformer windings under short-circuit conditions, a three-dimensional multiphysics field simulation model of the magnetic-fluid-thermal-structural coupling is constructed. The current magnitude under rated and short-circuit conditions of the transformer are calculated, the magnetic field distribution is obtained based on the current, and the loss is calculated as the heat source for temperature simulation. The temperature distribution results and short-circuit electromagnetic force are used as initial conditions for structural field simulation to analyse the elastic-plastic deformation, residual stress, and residual deformation of the winding under short-circuit impact. Considering the non-uniform distribution of winding temperature, compared with the results of the uniform temperature distribution, the maximum plastic strain of the winding increases by 84.6% after being subjected to short-circuit impact. The position of the wire cake where the maximum residual stress and residual deformation of the winding are located is different, and the values increase by 13.5% and 95.3%, respectively. Therefore, considering the non-uniform temperature distribution during the simulation process is more closely related to the actual working conditions, and thus more accurate in obtaining the location where cumulative deformation occurs.

The accurate prediction of mutual inductance in inductive planar coils is a critical challenge in advancing wireless power transfer (WPT) systems, particularly as traditional analytical methods struggle to balance precision and computational speed in complex, real-world scenarios. This study addresses these limitations by exploring data-driven algorithms for predicting mutual inductance. Additionally, the study offers a robust solution to handle the nonlinearities and dynamic requirements of three-dimensional coil configurations. Seven regression algorithms—linear, polynomial, kernel ridge, decision tree, random forest, support vector and neural network—are evaluated to identify the most effective approach. Key results reveal the superior performance of kernel ridge, support vector and neural network regression models, achieving R² scores of 0.995, 0.987 and 0.992, respectively. Kernel ridge regression demonstrated the lowest error metrics, with an MAE of 49.624 nH and an RMSE of 86.174 nH, whereas support vector and neural network regression followed closely with slightly higher errors. Conversely, traditional models such as linear regression and decision tree showed significantly higher MAEs and RMSEs, highlighting their inadequacy for handling the complexities of WPT datasets. This research establishes a scalable and accurate framework for mutual inductance prediction, paving the way for improved efficiency in WPT systems.

In this paper, the authors propose a novel active disturbance rejection current control method aimed at resolving inherent issues within the digital control system of an aircraft's permanent magnet synchronous motor (PMSM). These issues include a one-sample time delay and low-pass filter in the current measurement channel. The proposed method involves estimating the time delay using a predictive extended state observer (ESO) within the current loop of the PMSM to enhance anti-disturbance capabilities and robustness. Initially, a discrete-time model that takes current loop time delay into consideration is established. Subsequently, a predictive ESO is designed to estimate disturbances within the current loop. This ESO incorporates known model information to improve the accuracy and speed of disturbance estimation. Furthermore, compensation is made for the time delay stemming from the low-pass filter in the current measurement channel to enable real-time current detection. Lastly, the disturbance estimated by predictive ESO is combined with the feedback control law to form a model-enhanced active disturbance rejection current controller. The results from simulations and experiments validate the feasibility and accuracy of the proposed methodology.

The application of artificial intelligence in magnetic gear design has opened new avenues for accelerating computation and optimisation processes. In this paper, a Bayesian-optimised artificial neural network (ANN) was presented as a surrogate model to predict the performance of salient pole reluctance magnetic gears (SP-RMGs). The model focuses on key performance indicators such as average torque, torque ripple, and total weight. A diverse dataset generated through Latin hypercube sampling (LHS) is used to train the ANN, which employs customised activation functions to accurately capture the non-linear characteristics of the magnetic gear. Bayesian optimisation is applied to fine-tune the hyperparameters, resulting in a significant reduction in computational time. The proposed approach leverages deep learning to efficiently accelerate the multi-objective optimisation process, providing accurate predictions of SP-RMG performance metrics. The optimisation results demonstrate significant improvements with the model predicting optimal design parameters that enhance torque performance, reduce torque ripple by 47.2%, and decrease total weight. The proposed approach offers a substantial reduction in computational time while delivering precise optimisation outcomes.

This article proposes a new consequent-pole (CP) flux reversal permanent magnet (FRPM) motor with homopolar PMs parked in the stator slot openings and between the wound poles' teeth. The proposed PM pattern offers an effective PM usage and lowers the effective armature air-gap length while increasing the output torque. The operating principle of the motor is analysed using a flux modulation theory (FMT) and the predominant harmonics of the air-gap flux density are unveiled. Furthermore, the proposed structure is comparatively analysed with a CPFRPM and a conventional FRPM (CFRPM). These structures are optimised with a genetic algorithm, and by using the finite element analysis (FEA), the no-load and full-load performances are studied. Thanks to effective PM placement, the torque density is improved by 65% and 29% compared to the CFRPM and CPFRPM, respectively. PM demagnetisation, along with a thermal analysis, is developed to guarantee that the PMs are not prone to demagnetisation. A comparative study is also performed to highlight the outperformance of the proposed structure among the stator-PM motors. Eventually, the motor is prototyped and tested to validate the FEA predictions.

As the core equipment of the transmission system, the high hot spot temperature of the transformer will accelerate the ageing of the transformer, which will lead to thermal faults. It has become an urgent task to introduce digital technology to monitor and analyse the hot spot temperature in real time. In this paper, the magnetic, current and thermal multi-field coupling numerical analysis method is used to establish the digital model of the transformer and carry out simulation analysis, and the transformer loss data and temperature distribution are obtained. Infrared thermal image thermometer, thermocouple temperature measuring device and optical fibre temperature sensor are used to monitor the thermal state characteristic parameters of the transformer. From the two dimensions of multi-source and heterogeneous, a multi-source heterogeneous information fusion method is proposed to extract, clean, and filter experimental data. Taking 100 sets of data as the sample data, the first 80 sets of data are used as training sets to construct a BP neural network model optimised by the genetic algorithm, and the last 20 sets of data are used as prediction sets to test the model, so as to predict the hot spot temperature. By comparing with other algorithms, it is found that the evaluation constraint index of GA-BP is the smallest, MRE is 0.0651 and RMSE is 0.2158. MSPE was 0.44%. Combined with finite element analysis and external measurement data acquisition, a digital twin platform is developed to realise real-time evaluation and analysis of the transformer operation status, which is of great significance to the operation and maintenance of transformer equipment.

Synchronous machines are electric machines characterised by a rotating speed proportional to the AC supply and are independent of load. Traditional permanent magnet synchronous machines utilise rare earth minerals for rotor flux generation, posing challenges due to rising costs. In such conditions, wound rotor synchronous machines with electromagnets may offer several economic and operational benefits, particularly for applications requiring a wide range of speeds and better torque density. However, rotor excitation in these machines requires brushes and slip rings, which lead to higher initial and maintenance costs, increased sensitivity to fluctuations, reduced speed regulation, and lower power factor. Existing designs of brushless wound rotor synchronous machines use additional rotor windings, leading to increased complexity and reduced efficiency. Techniques such as third harmonic excitation have been employed to eliminate brushes, but these solutions often suffer from low starting torque and inefficient stator utilisation. To overcome the issues associated with existing brushless wound rotor machines, this article proposes a novel winding scheme that incorporates an additional stator winding to enhance the slot fill factor. Furthermore, the benefits of single and dual inverter-fed designs are analysed, highlighting the reduced cost, size, and simplified circuitry of the single inverter design, along with the improved control and performance offered by the dual inverter design. Finally, an advanced harmonic excitation method is integrated to optimise torque production. A thorough 2D finite element analysis has been conducted to validate the proposed designs and operational principles of the brushless wound rotor synchronous machine, demonstrating its potential for enhanced performance in various electric machine applications.

Offshore wind and solar energy have substantial attention for the generation of high-voltage ac (HVAC) and high-voltage dc (HVDC). However, traditional systems face application limitations due to low-frequency transformers and inefficient power converters. This article proposes a four-port solid-state transformer (FPSST) to enhance large-scale energy generation from renewable sources. The FPSST incorporates a modular multilevel converter to collect both medium-voltage ac and dc from wind and solar systems. Following this collection, high-frequency transformer-based dc/dc converters ensure galvanic isolation between ports by enabling a compact, lightweight and efficient design due to the advanced magnetic material. A cascaded H-bridges multilevel inverter produces HVAC with reduced harmonic distortion and precise voltage regulation. Moreover, a series-connected two-quadrant converter generates HVDC, providing a stable dc output through a discretised dc-link capacitor. The performance of the proposed FPSST is thoroughly investigated using the MATLAB/Simulink platform, which offers insight into system behaviour. Prior to experimental validation, an amorphous alloy-based high-frequency transformer is developed in the laboratory, and a 1-kW sealed-down FPSST is constructed. Simulation and experiment results confirm the feasibility and effectiveness of the proposed FPSST. Thanks to its compact, modular design, the four-port SST can be easily scaled, enabling both HVAC and HVDC generation from renewable sources.

The impulse frequency response method is often combined with the transformer winding equivalent circuit to jointly analyse the frequency response mechanism of the transformer and the winding state, while winding modelling is the necessary fundamental work. Therefore, this paper proposes a new approach to transformer winding modelling. Firstly, based on the design parameters of the transformer, a finite element model constructed using ANSYS Maxwell simulation software is used to calculate the capacitance, inductance, and resistance parameters of the transformer winding. Secondly, the model not only considers the capacitance effect between distant conductors and the mutual inductance between distant coils but also considers the frequency variation effect of parameters. Subsequently, an equivalent circuit model is formulated with the acquired parameters to analyse the frequency response characteristics of the winding. Finally, the equivalent winding model was subjected to various mechanical fault simulations across different operating conditions to investigate the alterations in the frequency spectrum curve under fault conditions. The results show that the simulated IFRA curve closely aligns with the measured IFRA curve, providing indirect evidence of the effective correspondence between the circuit model, which considers various influencing factors, and the actual physical model.

PM-assisted synchronous reluctance machines are becoming so attractive for electrical machine designers because of their lower cost due to minor usage of PMs in comparison to IPM and SMPM machines. Besides, they can effectively utilise both the magnetic and reluctance torques, thus, they can play an important role in many industrial applications including electric transportation systems. Up to now, more or less, the conventional distributed-windings are employed in design of the synchronous reluctance machines. However, recent advancements have seen the adoption of fractional-slot concentrated windings (FSCWs) due to their shorter end-winding length, simpler structure, and higher slot fill factor. Although FSCWs offer these advantages, they also suffer from higher magnetomotive force (MMF) space harmonics, which can lead to undesirable effects such as localised iron saturation and increased core losses. The main target of the present research was introducing the winding layouts, which have the benefits of both FSCWs and distributed windings to use in PM-assisted synchronous reluctance machine. To solve the problems related to FSCWs, in this research, the stator slot-shifting has been developed and new types of fractional-slot winding topologies, comprising significantly low MMF harmonics and short-end windings length have been proposed. Based on these proposed winding configurations, a prototype machine was built as case study; and analytical results, finite element analysis, and experimental tests were conducted to validate the machine’s characteristics. Although in the proposed machine the winding is converted to overlapped type and is not concentrated anymore, but the obtained results show significant advantages over the conventional design in terms of air-gap flux density, back electromotive force, torque profile, power factor, power losses, efficiency and flux weakening capability.