Iet Electric Power Applications最新文献

{"title":"Force Analysis and Torque Production of the Doubly Salient Parallel Path Magnetic Machine","authors":"Maryam Salehi,&nbsp;Madhav Manjrekar","doi":"10.1049/elp2.70150","DOIUrl":"https://doi.org/10.1049/elp2.70150","url":null,"abstract":"<p>An in-depth mathematical analysis of torque and force production mechanisms is presented in this paper for the Doubly Salient Parallel Path Magnetic Motor (DS-PPMM). This paper focuses on a comprehensive torque force analysis through analytical modelling and is verified by a detailed finite element analysis (FEA). Electromagnetic force distribution, torque ripple characteristics and the contributions of magnetic salience to overall torque production and performance of the machine are analysed. Tangential and radial components of the force vectors are computed using Maxwell's stress tensor. The results confirm the effectiveness of DS-PPMM in offering high torque density with low torque ripple.</p>","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.70150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptively Adjustable Control Compensation With a Decoupling Basis for Current Control of All Parametric Variations’ IPMSM","authors":"Shin-Hung Chang,&nbsp;Yi-Cheng Huang,&nbsp;Po-Cheng Hung","doi":"10.1049/elp2.70164","DOIUrl":"https://doi.org/10.1049/elp2.70164","url":null,"abstract":"<p>In this paper, a simply and adaptively adjustable control with decoupling technique is proposed for robust current control in an interior permanent magnet synchronous motor (IPMSM). Based on the Lyapunov stability theorem, all parametric variations in an IPMSM are thoroughly considered without knowledge of variation bounds. The system robustness, as well as stability is guaranteed. Combined with simple decoupling terms provide control voltage basis under known modelled parameters. Adaptively equivalent proportional and integral compensation control speeds up current tracking response under all parametric variations in an IPMSM, which greatly reduces the computational burden of a microprocessor. To verify its practical effectiveness, the proposed straightforward control algorithm is implemented using a TMS320F28377 DSP. Comparative simulations show the IPMSM under large variations’ assumptions. Experimental results show that overshoot in <i>q</i>-axis current can be roughly reduced from 10% to 1.5%–2% and overshoot in <i>d</i>-axis current can be roughly reduced from 50% to 5%, respectively. Steady time in <i>d-q</i> axes can be roughly reduced by 50%. It demonstrates the effectiveness and feasibility of the proposed control algorithm.</p>","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.70164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Damping Compensation for Active Vibration Isolation System With Active Disturbance Rejection Control","authors":"Cuicui Huang,&nbsp;Zikang Li,&nbsp;Chunhui Dai,&nbsp;Zhiqiang Long","doi":"10.1049/elp2.70145","DOIUrl":"https://doi.org/10.1049/elp2.70145","url":null,"abstract":"<p>In active vibration isolation systems (AVIS) with electromagnetic actuators, a fundamental trade-off exists between high-precision suspension control and effective vibration isolation. Although the classic active disturbance rejection control (ADRC) framework can estimate and compensate for disturbances, its conventional design tends to prioritise gap stabilisation without fully leveraging vibration feedback from the isolated object. This paper proposes a damping compensation-based ADRC (DC-ADRC) framework that integrates real-time vibration information from the isolated object to overcome this limitation. The extended state observer (ESO) is utilised to estimate disturbances in the relative position loop to ensure suspension stability, whereas a newly designed damping compensator actively incorporates absolute acceleration feedback to improve isolation performance. This new control strategy enhances both disturbance rejection and vibration isolation performance. Simulations and experimental results indicate that the controller provides outstanding vibration isolation performance, improving active isolation capabilities and effectively preventing mechanical collisions.</p>","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.70145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148076473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Scalable FPGA-Based Time-Division Multiplexing Architecture for High-Precision Synchronous Control of Large-Scale Micro DC Motors","authors":"Xilin Zhang,&nbsp;Bangji Wang,&nbsp;Mingrui Gou,&nbsp;Mengxuan Wang,&nbsp;Yong Huang","doi":"10.1049/elp2.70158","DOIUrl":"https://doi.org/10.1049/elp2.70158","url":null,"abstract":"<p>High-precision synchronous control of multiaxis motors is essential in systems that require large-scale high-accuracy motion coordination, such as phased array radar antennas. Traditional field-programmable gate array (FPGA) control architectures, however, typically employ a ‘single-axis single intellectual property (IP) core’ design, which faces inherent limitations. As the number of axes increases, the required logic resources grow linearly. This restricts the system's ability to support large-scale multiaxis systems on a single FPGA chip. Additionally, parameter variations among motors introduce complexity into the controller tuning. To overcome these limitations, this paper proposes an FPGA-based large-scale time division multiplexing (LTDM) method. This method determines the maximum number of axes that can be processed in one cycle by quantifying control time. Then, it designs a parallel time division multiplexing (TDM) architecture to expand the control scale. Cascaded buffering and pipeline segmentation are employed to address timing issues resulting from high fanout. To address the challenge of multimotor parameter tuning, a particle swarm optimisation (PSO)-based self-tuning strategy is developed, using a trajectory tracking evaluation function for automatic parameter adjustment. Experimental results demonstrate that the proposed method reduces logic resource consumption by 79.86% in an 88-axis micro DC motor servo system, with all motors maintaining position tracking errors within <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>±</mo>\u0000 <mn>2</mn>\u0000 <mo>°</mo>\u0000 </mrow>\u0000 <annotation> $pm 2{}^{circ}$</annotation>\u0000 </semantics></math>. This study exhibits strong scalability and application promotion value.</p>","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.70158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Induction Motor Multifault Detection Using Machine Learning: Improving Model Accuracy With Current Signal Preprocessing","authors":"Semen Koveshnikov,&nbsp;Nada El Bouharrouti,&nbsp;Alireza Nemat Saberi,&nbsp;Anouar Belahcen","doi":"10.1049/elp2.70160","DOIUrl":"https://doi.org/10.1049/elp2.70160","url":null,"abstract":"<p>Understanding optimal feature engineering for motor diagnostics remains an open research area, as many signal analysis and processing strategies have yet to be systematically explored and explicitly linked to the behaviour of machine learning models. This paper explores optimal parameters and techniques of current signal acquisition and preprocessing for detecting different faults in induction motors using machine learning and motor current signature analysis. Conventional fault detection methods usually advocate for a higher frequency resolution in the motor current spectrum, which requires longer current signal measurements that are difficult and expensive to conduct and process in case of a real-world scenario. Thus, this work aims to identify the limitations to frequency resolution for successful fault diagnosis when applying machine learning algorithms, here, a multilayer perceptron model. The goal is to provide recommendations on signal preprocessing for feature extraction to enhance the performance of the machine learning model. For example, a signal transformation method via signal multirate resampling is evaluated for the improvement of the algorithm accuracy results. The proposed methods boost not only the prediction accuracy but also may refine the system performance when considering industrial applications, as this work outlines in detail the signal acquisition and preprocessing logic. In this paper, the investigated faults include broken rotor bar, bearing fault, misalignment, eccentricity, imbalance, looseness and mixed faults. This article also proposes a new outlook on machine learning feature introspection with scatter plots, which are surprisingly rarely used in the domain of motor condition monitoring but offer a swift access to data insights at the stage of feature engineering.</p>","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.70160","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Indirect Measurement Approach of a Uniform Transformer Winding Inductances Using Frequency Response Analysis Data: An Analytical Approach","authors":"Moustafa Sahnoune Chaouche,&nbsp;Faouzi Didi,&nbsp;Aymen Flah,&nbsp;Hamza Houassine,&nbsp;Slim Ben Chaabane,&nbsp;Rachid Sahnoune Chaouche,&nbsp;Mohd Fairouz Mohd Yousof,&nbsp;Mohamed K. Metwaly,&nbsp;Sherif S. M. Ghoneim","doi":"10.1049/elp2.70155","DOIUrl":"https://doi.org/10.1049/elp2.70155","url":null,"abstract":"<p>This article proposes an innovative analytical method that estimates winding inductances directly from measurable frequency response analysis (FRA) data. The method constructs a polynomial function based on the inverse-square sum of short-circuit natural frequencies and equivalent inductance, incorporating a coupling factor that captures the extent of mutual interaction between discs. Unlike earlier numerical and optimisation-based methods that typically involve a large number of inductance parameters, the proposed approach yields a physically meaningful real positive solution for the coupling factor. The approach offers a practical analytical solution for indirectly measuring the self- and mutual inductances of uniform windings, involving the measurement of relevant winding parameters—such as equivalent inductance, short-circuit natural frequencies and series and shunt capacitances—using frequency response impedance data. The method then estimates self- and mutual inductances for uniform windings by solving a generalised polynomial developed for an <i>N</i>-section model, enabling the derivation of all windings' inductances. Experimental validation on isolated transformer windings demonstrated strong agreement between measured and estimated FRA curves, with root mean square error (RMSE) values below <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mn>10</mn>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${10}^{-3}$</annotation>\u0000 </semantics></math>. These results confirm that the method offers a practical, traceable and accurate measurement-based solution for inductance estimation, with potential applications in transformer condition assessment and fault diagnosis.</p>","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.70155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Image-to-Image Translation Model for Ultra-Fast Performance Prediction of Permanent Magnet Synchronous Machines","authors":"Mengyu Cheng,&nbsp;Xing Zhao,&nbsp;Guangjin Li","doi":"10.1049/elp2.70161","DOIUrl":"https://doi.org/10.1049/elp2.70161","url":null,"abstract":"<p>Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown great potential in accelerating computational tasks. Notably, image-to-image translation (I2I) has gained significant attention due to its broad applications in computer vision and image processing problems. This paper presents a data-driven I2I model based on conditional generative adversarial networks (cGAN) to predict the performance of an interior permanent magnet synchronous machine (IPMSM). The generator of the cGAN can plot tangential and radial magnetic flux density maps from the input cross-sectional images of an IPMSM with varying geometric features, dimensions, pole-slot configurations and excitations. Additionally, a comparison is made between traditional cGAN networks and a modified multidomain version that incorporates a physics-informed loss function. Model test shows that the proposed method significantly reduces computation time. For a single design, the improvement of evaluation speed is approximately 90.23% compared to the FEA simulation. Besides, the multidomain evaluative model can reduce training time consumption whilst improving the prediction accuracy. Experimental validation indicates that the predicted magnetic flux density maps have an average error of less than 0.13% compared to FEA results. Furthermore, post-processing the generated images enables the calculation of the torque, flux linkage and induced voltage of the IPMSM. The accuracy of performance calculation using the I2I model is also verified against data obtained from FEA software.</p>","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.70161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Suppression of DC Winding Current Harmonics in Wound-Field Machines Under PWM Excitation by Using Active Voltage Injection and LC Low-Pass Filters","authors":"Zhimian Wang,&nbsp;Lai Jin,&nbsp;Zhongze Wu,&nbsp;Wei Hua,&nbsp;Wentao Zhang,&nbsp;Ming Cheng","doi":"10.1049/elp2.70157","DOIUrl":"https://doi.org/10.1049/elp2.70157","url":null,"abstract":"<p>The magnetic field modulation wound-field (MFMWF) machine exhibits good rotor robustness similar to switched reluctance machines. However, the DC winding induced voltage pulsation has been one of the major problems in MFMWF machines. Moreover, pulse width modulation (PWM) excitation further introduces harmonics in the DC winding induced voltage around the PWM frequency and its integer multiples. In this paper, a suppression method composed of active voltage injection and an LC low-pass filter with passive damping is proposed for the first time to suppress the influence of the DC winding induced voltage pulsation, that is, the DC winding current harmonics. To verify the effectiveness of the proposed method, a prototype is built and tested. The experimental results show that the proposed method can effectively improve the suppression capability of the DC winding current control system for both the baseband-order harmonics and the sideband-order harmonics.</p>","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.70157","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic-Thermal Coupling Analysis of the End Region of a Large Steam Turbine Generator in the Phase Advancing State Based on the Improved TSO and SVR Regression Models","authors":"Yonggang Li,&nbsp;Fuchuan Guan,&nbsp;Tianyu Yao,&nbsp;Peng Qi,&nbsp;Minghan Ma","doi":"10.1049/elp2.70141","DOIUrl":"https://doi.org/10.1049/elp2.70141","url":null,"abstract":"<p>The accurate magnetic-thermal analysis of generator end regions under leading-phase operation (LPO) remains challenging due to intensified magnetic field fluctuations caused by strong armature reaction. Conventional methods often struggle to achieve high precision under these conditions. To address this gap, this paper proposes a novel hybrid regression model that integrates an improved Tuna Swarm Optimisation (TSO) algorithm with Support Vector Regression (SVR). This approach represents a significant advancement over existing single-method analyses. The study begins by establishing a high-fidelity 3D magnetic-thermal simulation and a coupled field-circuit-network model for a 320 MVA steam turbine generator. This model is used to compute the stator end magnetic field distribution under various leading-phase depths. The core novelty lies in the application of the improved TSO algorithm to optimise the hyperparameters of the SVR model, creating a robust framework for magnetic field prediction under the complex conditions of LPO. The optimised magnetic field data are then utilised for temperature field analysis. Validation against experimental data from a high-power test platform demonstrates the superiority of the proposed TSO-SVR model. The results show a significant improvement in prediction accuracy compared with conventional methods and other benchmark algorithms, with temperature trends closely aligning with measurements. The primary contribution of this work is the development of a highly accurate and reliable coupled electromagnetic-thermal analysis methodology, which provides a more effective tool for the design and safe operation assessment of generators in LPO, a critical capability for modern power systems with high renewable energy penetration.</p>","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.70141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}