Iet Electric Power Applications最新文献

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.

This paper proposes a novel self-cooling solution for surface-mounted permanent magnet machines, which are widely used in various industry sectors. By properly designing a propeller and integrating it into the rotor structure, leading to a blade-structured rotor design, the self-cooling capability is achieved without the need for rotor wafters or rotor mounted fans. When rotor rotates, the cooling air (coolant) is drawn into the machine through inlets and expelled from the outlets, both inlets and outlets can be in the endplates or in the housing. During this process, air will thoroughly contact various internal components, such as end-windings, stator and rotor iron cores, along its flow path. As a result, internally generated heat in the windings and in the rotor mounted permanent magnets will be removed effectively. The study focuses particularly on the hot spots (locations with highest temperature) along the airflow path, such as the end-windings and permanent magnets. Different factors that affect the efficacy of this self-cooling solution, such as the number of propeller blades, position and size of inlets and outlets and rotor rotational speeds, are studied and compared. These studies are initially based on 3-dimensional computational fluid dynamic models and later validated through a series of experiments.

The dual three-phase rectifier synchronous generator (DTP-RSG) is commonly used in areas such as electric vehicles, aviation and wind power generation because of its high power supply quality, good electromagnetic compatibility and strong fault-tolerant operation. However, when the load changes suddenly, the voltage at the DTP-RSG terminal fluctuates accordingly, which can negatively affect the overall performance of the power supply system. An electrolytic capacitor serves to store energy and stabilise the output voltage and is therefore commonly employed as a filter capacitor in DC power supply systems. The parasitic parameters, including the equivalent series resistance (ESR) and equivalent series inductance (ESL) of the filter capacitor, significantly influence the system's output. For this reason, this paper adopts an agent-aided optimisation method based on heuristic optimisation and intelligent algorithms to study the influence of the filter capacitor and its parasitic parameters on the output performance of the DTP-RSG. The objective is to reduce the difference between transient voltage and steady-state voltage, reduce the DC voltage ripple coefficient and improve efficiency to find the most suitable filter capacitor. Through experimental verification, the transient voltage mathematical model established in this paper has high accuracy. The results of the agent-aided optimisation method demonstrate that considering the parasitic parameters of the filter capacitor is crucial for analysing the output performance of the DTP-RSG, and selecting an appropriate filter capacitor can improve the power quality of the entire DC power system.

Vibration signal analysis plays a vital role in the condition-based preventive maintenance of induction motor by identifying early signs of motor issues, avoiding costly breakdowns and optimising the motor's maintenance schedule. It provides detailed information very useful for extending the motor's life cycle with proactive, condition-specific maintenance. Furthermore, the vibration signal analysis offers the advantage of identifying the health status of rotating machinery as a whole, as well as its individual components. This paper presents an innovative solution for the automated health assessment of a critical induction motor component: the bearing. Our approach uses the matrix pencil method for signal processing and health signature generation, combined with a multilayer perceptron neural network to detect health conditions from the resulting health signature characteristics. Initially, the matrix pencil is applied to the vibration signal to identify the mean frequency characteristics. This vector provides a holistic view of the signal’s inherent features and transforms its frequency characteristics into a visual spectrum, resulting in improved induction motor bearing fault condition monitoring. Subsequently, the output from the matrix pencil mean frequency analysis is processed by a multilayer perceptron neural classifier, chosen for its low computational cost and high classification accuracy. Experimental validation demonstrates a 100% fault classification rate and automatic identification of defective components. Comprehensive validation further confirms the method’s robustness and feasibility for induction motor bearing fault detection compared to other recently methods.

This paper presents a modified structure of synchronous reluctance motors (SynRMs) combined with a new design strategy in which hybrid optimisation is considered to enhance line start capability and power factor. The rotor design, along with some embedded permanent magnets (PMs), has an important effect on the static performance. In the proposed SynRM, the rotor bars are designed for the flux barriers, and some additional bars are considered near the q-axis. The PM arrangement is changed so that some additional PMs are considered. In the proposed design strategy, two optimisation procedures called local and global optimisation phases are considered. In the first step, genetic algorithm (GA) is used to obtain optimal geometric parameters for some performance metrics. In the next step, the global response surface method is applied to combine the mentioned metrics and determine the globally optimal design for both stator configurations. In the last optimisation process, improvement of line-start capability and power factor are proposed as the main goals. The analysis results show that the design proposed by SynRM has better performance in terms of line-start capability and power factor. Experimental validation of the proposed SynRM is also presented to confirm the accuracy of the finite element method (FEM) simulation.

Hashem Yousefi Javid and Aydin Yousefi Javid (2025). Modelling and Simulation of a Novel Axial Flux Permanent Magnet Hysteresis Motor Comparing Different Disc Structures. IET Electric Power Applications. 19. https://doi.org/10.1049/elp2.70008.

In Section 3.3 of the original publication, the reference to Equations 1 and 2 was incorrect. This has been updated to correctly refer to Equations 8 and 9. Furthermore, Equations 8 and 9 have now been accurately presented to the following:

We apologise for this error.

Large variable-speed pumped storage generator-motors (VSPSGMs) offer superior speed adaptability and rapid adjustment capabilities for both active and reactive power. However, their rotor AC excitation systems pose significant challenges for electromagnetic design and ventilation cooling due to variable operational conditions. In this paper, the electromagnetic parameters, ventilation cooling system and heat transfer mechanism of 10 MW VSPSGM are studied. Losses in end components of VSPSGM are obtained at different locations. Fluid velocity distributions are analysed under different speeds. A coupled fluid-thermal model is developed for the end region of the 10 MW VSPSGM. Complex fluid flow and temperature distribution of end components in the VSPSGM are studied and compared at different speeds. The location of the highest temperature point is obtained. The calculated results are compared with the experimental test values. The ventilation cooling system design and accuracy of the calculation method are verified.