This paper presents an innovative methodology for modeling and predicting high-frequency harmonic losses in the rotating field of an electrical machine powered by a frequency converter. Conventional approaches are often unreliable because these losses depend heavily on switching frequency, modulation index, load conditions, and transient inductance, all of which can vary significantly. To address this challenge, a data-driven surrogate modeling approach based on Gaussian process regression is proposed. The training data were obtained from measurements on three induction machines with rated powers of 15 kW, 37 kW, and 75 kW, all tested under the same relative load conditions. The frequency converter was configured with varying reference switching frequencies, and under each condition, the additional high-frequency harmonic losses induced by the pulse-width modulation supply were quantified through waveform analysis. Model performance was validated using a portion of the measurement data reserved for testing. The results demonstrate that the proposed methodology predicts additional harmonic losses with high accuracy and provides valuable insights for the design of more efficient inverter-fed drive systems

Topology optimization often produces unfeasible geometries, making manufacturing challenging. This paper addresses this limitation through the development of a methodology that utilizes a normalized Gaussian network (NGnet) with Gaussian functions of variable sizes unevenly distributed within an optimized space. The overlap of the Gaussian functions is analyzed for its impact on the final geometry. In addition, several modifications are applied to the NGnet output to achieve feasible geometries. Triangular elements are used to smooth the final geometry, enhancing its manufacturability. The matrix representation is used as a computational encoding for topology search and constraint checking, while the output is intended to be transferable to standard CAD and conventional manufacturing routes (laminated electrical steel).The main contribution is the integration of manufacturability constraints and geometry-repair operations before FEA evaluation, which reduces the number of infeasible candidates explored by the optimizer. The approach is demonstrated on a line-start synchronous reluctance machine rotor case study and can be extended to other electrical machine rotor topologies requiring multi-material feasibility constraints.

The primary characteristics of an electric drive include the ability to utilize the maximum torque throughout the speed range, minimal reliance on parameters, and, if feasible, the highest efficiency. This paper proposes a new speed observer and energy-saving control approach for induction motors (IMs) that exploits the advantages of Maximum Active Power per Flux Controlling Variable (MAPPFCV) for flux optimization. It employs an extended-speed observer to enhance the low-speed stability of sensorless IM and address the challenges, reducing the unstable area during low-speed operation. The proposed energy-saving approach, in real time, enables the algorithm to evaluate the optimal flux of the MAPPFCV, thereby enhancing efficiency. In a recent study on applying the loss model for flux optimization, the core loss resistance has been considered, as it increases the robustness and accuracy of the model. However, considering core resistance, complexity, and the flux optimization parameter dependence increases, resulting in significantly increased sensitivity. Thus, this paper proposes a model-based approach that considers core loss resistance for flux optimization, providing better performance while being less complex and robust to parameter variations. The advantages obtained, including simple and straightforward implementation; high dynamic performance; reduced stator current drawn by the drive; reduced power loss leading to improved efficiency, and reduced steady-state torque ripple, are presented in the article. The findings demonstrate that the proposed approach performs well across various operating conditions. Hardware results on a 5.5 kW IM are presented to validate the effectiveness and performance of the proposed approach.

ABSTRACT Multiphase field‐wound flux switching (FWFS) machines combine the advantages of traditional flux‐switching permanent magnet machines with the benefits of multiphase architectures, including enhanced flux controllability, high fault tolerance, increased torque density and reduced torque ripple. The doubly salient structure contributes to mechanical robustness and structural simplicity, while the utilisation of non‐overlapping windings reduces the volume of copper, which also minimises the production costs of FWFS machines. This paper presents a novel five‐phase outer rotor FWFS machine with non‐overlap windings and investigates four different rotor pole topologies. All the designs are analysed with respect to flux linkage, back‐EMF, cogging torque and electromagnetic torque. Among these configurations, the 10 slot/11 pole configuration demonstrated superior performances, exhibiting high flux linkage and average torque and minimum cogging torque. A deterministic optimisation technique was applied on 10 slot/11 pole topology to further enhance its electromagnetic performance, resulting in a 44.8% increase in average torque along with significant improvements in other parameters. The optimised design was then compared against the existing inner rotor design, showing a 96% increase in torque output. Additionally, the rotor pole designs were optimised to suit direct‐drive applications, confirming the proposed five‐phase design's potential for high speed and high reliability use in electric vehicles and renewable energy systems.

This work introduces an innovative application of established machine learning methods to calculate transformer no-load losses. An accurate estimation of losses is crucial to cost-effective and reliable transformer design. Existing methods often lack precision due to hard-to-predict additional losses or are computationally expensive due to complex finite element models. The proposed method enables fast and accurate prediction of transformer no-load losses using Gaussian process regression, selected for its suitability for the dataset and problem. Building on previous work, an improved surrogate model is developed with enhanced outlier filtration and optimized input selection. The approach is further extended to predict a range of losses, accounting for manufacturing deviations. Its performance is validated through a learning curve. Key findings indicate that outlier removal significantly improves accuracy and the optimal model depends on nine key variables from an extended input set. Modeling ranges of losses is feasible with a sufficiently large dataset, which further enhances accuracy, as confirmed by the learning curve. Overall, the method shows a strong potential for accurate no-load loss estimation and to support more efficient and cost-effective transformer design.

Nowadays, the main focus of the design process of electrical machines is typically electromagnetic analysis. However, as requirements have increased for electrical machines and drives with specific uses and higher efficiency, thermal analysis has become a more significant part of the design process. The temperature rise within a machine influences its output power and can also lead to the thermal degradation of significant parts of the machine, in particular the winding insulation and permanent magnets, if these are used in the machine construction. A precise estimate of temperature is crucial for the protection of critical machine components, which in turn requires an accurate prediction of machine temperatures. This can be achieved through various research methods, such as finite element analysis or analytical approaches, along with appropriate analysis methodologies. In this work, Lumped Parameter Thermal Networks are used. This analytical method is very convenient due to its need for low computing time and fast optimization execution. This publication focuses on the optimization of heat transfer coefficients using a genetic algorithm, which is a key factor in achieving accurate thermal analysis. Various methods for the estimation of these coefficients are evaluated and incorporated into the optimization process. In addition, graphical outputs of the calculations, including comparisons of the calculated and measured temperatures, for different methods used to approximate the heat transfer coefficients, are also presented in this paper. The measured temperatures were obtained on a fully automated test bench under stable conditions. The paper concludes with a discussion of the deviations between individual results, highlighting their impact on overall optimization accuracy. Moreover, an improved methodology for thermal analysis is suggested, enabling real-time, sensor-less temperature predictions.

High-speed machines are popular in industry due to their high power density. Permanent synchronous machines are commonly preferred, but they rely on rare earth magnets, which are expensive and environmentally demanding. Axially laminated anisotropic synchronous reluctance machines, a promising alternative, are currently limited to two poles and simple rotor geometry. This paper proposes a modified axially laminated anisotropic geometry, produced using multi-material additive manufacturing, to overcome these limitations in a high-speed synchronous reluctance machine. Magnetic 17-4PH and non-magnetic 316L steels were selected as suitable materials for a case study machine. To determine the effect of heat treatment on the magnetic properties of an additively manufactured 17-4PH steel sample, magnetic measurements were conducted. A 60 000-rpm case study machine was optimized using a non-dominated sorting genetic Algorithm 2, resulting in a machine with a shorter active length. Tests conducted during and after manufacturing verified the feasibility of the proposed rotor solution. This development expands the potential use of axially laminated anisotropic topology and synchronous reluctance machines in high-speed applications. Additionally, the successful use of multi-material additive manufacturing technology in the field of electrical machines is demonstrated.

Solid-rotor induction machines have gained significant attention in various industrial applications due to their robustness, reliability, and cost-effectiveness. This paper presents a comprehensive overview of these machines, covering their classification and various applications. The paper starts with discussing the widespread usage of solid-rotor induction machines in numerous industry sectors, including manufacturing, transportation, and renewable energy generation. The ability to operate under harsh environmental conditions and in safety-critical settings has made these machines indispensable in many fields of engineering. Their detailed classification based on different rotor topologies is provided, highlighting the unique design features and performance characteristics of each category. Simple and hybrid configurations and their distinct advantages and limitations in specific applications are included. This paper further explores the essential aspects of multi-physics modeling of solid-rotor induction machines, incorporating electromagnetic, mechanical, and thermal considerations to gain deep insights into the complex interactions between components and to guide the optimization process for enhanced performance and efficiency. This work is intended as a valuable reference for researchers and engineers seeking a comprehensive understanding of solid-rotor induction machines, from their diverse applications to the intricacies of their electromagnetic, thermal, and mechanical modeling. By shedding light on these aspects, this work contributes to the advancement and utilization uptake of these machines in modern industrial settings.

High-speed induction machines (HSIMs) and their associated converters have seen significant advancements over the past decade, driven by the increasing demand for high-efficiency solutions in industrial and compressor applications. This paper provides a comprehensive review of these advancements. By consolidating the findings from numerous studies, the full overview aims to identify critical challenges, research gaps, and opportunities for future development. The findings aim to guide researchers and practitioners in addressing the evolving requirements of high-power density and high-speed applications.

The growing trend in the electrification of all modes of transportation has led to an increasing demand for electrical machines specifically $d$ eveloped $f$ or $t$ his sector. $T$ his has resulted in advancements in electrical machines over the past few decades, primarily intended for e-mobility applications. This paper presents a review of these machines, with a focus on their applications in the automotive sector. The paper discusses the challenges and requirements for these machines, as well as the main technological themes. Additionally, a brief overview of current technologies is provided, and some real topologies are compared. This work is intended as a reference for researchers and engineers seeking information in this rapidly developing sector.

This paper delineates the process of design, modeling, and multi-objective optimization of a compact induction machine tailored for pump drive applications. The primary objective is to enhance critical performance parameters-specifically efficiency and $t$ hermal $c$ haracteristics-while a dhering $t o$ manufacturability constraints prevalent in industrial settings. A systematic methodology is proposed, encompassing both electromagnetic and thermal modeling, alongside an optimization strategy utilizing the Non-Dominated Sorting Genetic Algorithm II. Furthermore, the study encompasses the development of prototypes and their subsequent experimental validation, ensuring that the final design m eets established performance criteria and practical implementation standards. This research underscores the potential for significant energy savings through optimization, even in low-power electrical machines that are often neglected regarding efficiency enhancements.

The improvement of efficiency is one of the main areas of research in electrical machines today. Three-phase induction machines are widely used in industry, motivating researchers to explore methods to increase their efficiency or replace them with more efficient alternatives. A line-start synchronous machine is a promising machine design for achieving high-efficiency classes even for low-power applications. The main contribution of this paper is to show the achievable performance of the LSPMSM by simply replacing the rotor of an induction machine. In this work, two different geometries suitable for low-power machines are optimized and studied in terms of electromagnetic parameters. Finally, the results of the start-up simulations for both machine designs are presented. The presented method shows that by replacing the induction machine rotor with a line-start permanent magnet rotor, it is possible to significantly enhance machine efficiency, even for low-power applications.

In electrical engineering, a design of electrical machine using numerical methods, such as Finite element method, is a common practice. Electrical machines are complex multi-physical systems where for finding the optimal sets of designs solutions, called Pareto fronts, a very effective approach is to use multi-objective optimization. The most popular method for multi-objective optimization of machine design is the use of numerical optimization algorithms such as NSGA-II. However, due to the time-consuming nature of induction machines simulations, this approach is not very effective. This paper addresses this issue by proposing machine learning as a solution, specifically utilizing Multi-objective Bayesian optimization. This optimization method has been used in many industries as an efficient global optimization of the modeled system. By using the right acquisition function, the search space can be efficiently navigated to find the optimal candidates. Moreover, the optimization requires only a limited number of samples. The main aim of this paper is to present this method, which is demonstrated on the optimization of a 1.5 kW induction machine with time-consuming calculations. The machine optimization approach is not the main focus here, as this method can be effectively applied to any machine design or even any optimization approach. Furthermore, two possible approaches of machine optimization using this method are presented here.

This paper explores the innovative application of machine learning to calculate no-load losses in transformers. Existing calculation methods are either insufficiently accurate, as they do not consider all factors contributing to additional losses, or computationally slow due to the complexity of finite element method models. The accurate determination of these additional losses is crucial for a correct transformer design. The proposed calculation method offers a fast and highly accurate prediction of losses, utilizing training data consisting of measured values of manufactured transformers, providing the optimal source for modeling reality. Gaussian process regression is chosen as the learning technique because of the limited number of samples. Three surrogate models are presented, each providing a single output value that represents the percentage of additional losses to the nominal core loss. For further validation, the results of the surrogate models are compared with the analytical calculation. The nominal core loss can be calculated as the product of the core weight and the specific loss. The first model incorporates four input variables: nominal core loss, leg pinch, window height, and core cross-section area. The second model expands to include three additional inputs: steel grade, calculated flux density, and maximum steel sheet width. Both models employ variables with continuous kernels. Because the second model with seven inputs predicts new data slightly less accurately, a third similar model is introduced. In this third model, one variable, steel grade, utilizes a categorical kernel due to its specific behavior. This minor adjustment improves the accuracy of the prediction compared to the test data. Moreover, all presented models exhibit significantly higher accuracy compared to the analytical calculation.

Induction motors consume huge amount of energy, so their energy efficiency is an important topic. The efficiency determination methods of direct-on-line motors are mature and during the last decade efforts have been made to create a standardized way to also determine efficiency of converter-fed motors. Here the efficiency determination methods given in IEC 60034-2-3 are utilized using a modern induction motor with the rated power of 7.5 kW and compared with the sinusoidal supply results. In addition, the high frequency harmonic power is extracted from the measured active power and its behavior as a function of the frequency and torque is illustrated.

This paper describes a method for analysing high frequency harmonic losses in the rotating field machines supplied with a frequency converter. The loss segregation is mainly used in the energy efficiency classification and analysis of direct-online induction motors. Previously, there has not been a universal method to segregate the additional high frequency harmonic losses created by the PWM supply from the experimentally determined total losses of the 37-kW modern IE3 rated converter-fed induction motor. This paper demonstrates the method to segregate and analyse the high frequency harmonic losses created by the PWM supply based on the assumption that the loss component is equal to harmonic power determined from the electric power measurement from the motor terminals. The loss segregation is performed in 30 operations points in the frequency - torque plane. The drive system losses are segregated into the loss components, converter losses, motor losses, and high frequency harmonic losses.

This paper presents the concept of a modular high-speed solid rotor induction machine. The utilization of this type of machine is mainly for the verification of the solid rotor manufacturing technology, where it is necessary to verify whether the rotor can withstand centrifugal forces at nominal speed. Due to the very simple construction of the machine, enclosed in a frame that can be easily assembled and disassembled, the rotor can be replaced very easily. Therefore, it is not necessary to create a prototype for another machine but to verify the technology for the production of the solid rotor. In this case, copper coated solid rotor technology is used. The concept and dimensions of the machine are also presented here, including the calculated electromagnetic parameters and rotor dynamics of the machine, using numerical methods. The advantage of utilizing the machine is in the reduction of the cost of producing different machines.

This paper deals with the design of a single-phase line-start permanent magnet synchronous machine. The parametric optimization is used for achieving improved parameters of the single-phase line-start permanent magnet synchronous machine compared to commonly used induction machines. The optimization includes a time-consuming transient analysis considering the machine’s starting process and its synchronizing capabilities. One selected optimized design is analysed in detail and the results are discussed.The main contribution is a developed procedure for the optimization of the single-phase line-start permanent magnet synchronous machine of small power with class efficiency in IE2. Moreover, the manufacturability and the final material consumption are considered during the optimization.

The demand for low-loss and low-cost magnetic bearings is high. Currently, prevailed reliable solutions for the magnetically supported rotor are active or partially passive (hybrid) electromagnetic bearings. Nevertheless, those solutions require power electronics to supply the current into coils that create electromagnetic forces acting on the rotor. On the other hand, electrodynamic bearing works on the principle of electrical induction of eddy currents in the conductor. As can be deduced, the supporting forces are generated only if the change in the magnetic field is present.In this paper radial homopolar electrodynamic magnetic bearing is analysed. The advantage of homopolar electrodynamic bearing is in low-loss operation due to fact that currents in the conductor are induced only if the rotor is off-centred. Quasi radial magnetisation has been provided by a pair of opposing axial magnets. With the use of radial magnets in various arrangements instead, electrodynamic bearing reaches promising parameters in sense of stiffness and damping.

This article introduces the design and optimization of line-start synchronous machines in fractional horsepower industry applications. The proposed optimized design will be considered as a replacement for the induction machine driven from an ac drive. The optimized design will be achieved using a 2-D finite-element transient analysis and multi-objective optimization algorithm. For the optimization, a multi-objective self-organizing migrating algorithm was chosen. The optimization results are compared with the more popular nondominated sorting genetic algorithm (NSGA-II) algorithm, to prove algorithm sufficiency with three objectives and five design parameters in the area of the electromagnetic design application.