Performance Of Electrical Machines Research Articles

Influence of welding-induced mechanical stress on the electromagnetic properties of magnetic lamination

PurposeThis paper aims to analyze the influence of welding-induced mechanical stress of a magnetic core material on the performance behavior of a permanent magnet excited synchronous machine (PMSM). Welding, interlocking, clinching and the use of adhesives are state-of-the-art packaging technologies used in the manufacturing of electrical machines. However, the packaging processes degrade the electromagnetic properties of the electric steel sheets, thereby decreasing the performance and achievable range of the electric vehicle.Design/methodology/approachIn this paper, an approach that maps the local changes in magnetic properties due to welding induced stress with the stress values is developed. The welding process induces internal stress inside the steel sheet due to the diffusion of thermal energy into the sheets. Other effects are the changes in the micro structures of the steel sheets (grain sizes). These induced mechanical stresses lead to significant deterioration of the electromagnetic properties. They also lead to an increase in iron loss attributed to steel lamination.FindingsA low speed (city), a high-speed (highway) and WLTC-c3 driving cycle will be used to analyze the effects of the induced stresses on the machine efficiency at the different operating conditions. A high-speed PMSM with a maximum speed of 26,000 min−1 and maximum torque of 130 Nm is designed for this study.Originality/valueThe value of this study is in the development of a local varying modeling approach that analyses the influence of weld-induced stress on the performance of electrical machines. Its originality is evident in the mapping methodology. This will enable an application dependent improvement possibilities due to the understanding of the impact of weld-induced stress on the electromagnetic properties of weld-packaged core.

Tuned Windings: the window of opportunity for the implementation of solid conductors in high frequency cryogenic electric machines

Optimization techniques can significantly improve the performance of electrical machines. The analysis and the optimization algorithms focus on computational efficiency. In this paper, the optimization of a high frequency electrical machine in cryogenic conditions is proposed. The losses introduced by AC current in a conductor are directly proportional with the applied frequency in terms of skin effect and proximity effect. The stator coil, made from two aluminium (Al) turns, in the presence of cryogenic coolants has been analysed at frequencies up to 1000 Hz. In order to reduce the AC losses introduced by the magnetic fields, Nonlinear Conjugate Gradient (NCG) method has been applied for the stator coil thickness optimization. Frequency dependence is observed in most simulations. In the presence of Liquid Hydrogen (LH2) coolant, Al 1100 conductor presents the minimum power loss.

A novel approach towards parametric assessment of reliability and resilience of high voltage mica‐based insulation systems by statistical analysis of experimental failure data

AbstractInsulation systems in high‐voltage electric machines play a pivotal role in the reliable operation and longevity of the equipment. Mica‐based insulation materials have proven to possess and maintain excellent dielectric properties in the long run and prevent premature insulation degradation. Numerous qualifications tests, such as voltage endurance, are outlined in IEC and IEEE standards. The authors, however, take a different parametric approach, opting for reliability assessment of insulation systems using derived three‐parameter Weibull models. Therefore, instead of simple pass–fail criteria, empirical data is employed to determine failure rate probabilities quantitatively and objectively. Experimental data, including breakdown, dissipation factor, and partial discharge measurements, are used to construct the Weibull distribution model to predict fault and failure rates and calculate hazard functions. The rigorous examinations interpreted through the analytical model help assess insulation system resilience and particularly the impact of electrical field stress and mica content. Variation of electrical stress from 66.75 to 71.20 V/mil demonstrated how the mean time to failure of the system changed from 146.4 to 85.1 at 3 Un, hence identifying opportunities for design improvement and uncovering performance boundaries. Ultimately, the developed framework enhances comprehension of insulation system failure probabilities, guiding design decisions and ensuring a secure and reliable operation of electrical machines across applications.

Implementation of Ventilation Panel Installation

Abstract. Electrical panels are an important component in industry, which provides security to electrical installations in various industrial sectors. Large-scale companies dominate this industry to meet the high needs of consumers and maintain the quality and security of electrical installations. Electric panels must be installed carefully, pay attention to the surrounding environment, and play an important role in controlling ventilation to regulate the temperature and air conditions inside the cage. This research uses qualitative methods to understand the process of making ventilation panels. The results cover two types of panels: electric panels and ventilation panels. Electric panels serve as storage for electrical components, protect from the surrounding environment, and facilitate the operation of electrical machines. Meanwhile, ventilation panels are a crucial part of modern farm management, adjusting temperature and humidity to suit the needs of animals. The components in the ventilation panel involve various elements such as MCBs, TORs, contactors, relays, busbars, and others. The process of creating ventilation panels includes steps such as sketching pictures, making electrical panel boxes, assembling components, busbar assembly, wiring, as well as QA and QC stages to ensure panel quality. This process ends with finishing and packing before delivery to the consumer. This research provides an in-depth understanding of the manufacture of ventilation panels, which is essential for modern industry and agriculture inining optimal environmental conditions.

Online Detection and Classification of Interturn and Groundwall Insulation Aging Based on Broadband Common-Mode Impedance Spectrum

Online condition monitoring for stator insulation is important for reliable operation of electrical machines. Recent studies show that the method based on common-mode (CM) impedance spectrum is effective in online monitoring of the overall insulation ageing condition. However, it is difficult to detect the inter-turn ageing condition since the groundwall insulation ageing has the similar impact on the monitoring results. In this paper, the relationship between the change in the common-mode impedance spectrum and the stator winding insulation ageing is studied based on the per-turn-unit broadband CM stator winding model. The dataset of impedance spectrum for different insulation ageing conditions is built using this model. Multiple points in the impedance spectrum are used to detect the change in the inter-turn and groundwall insulation parameters separately. The online experiment results prove the validity of the proposed method. The proposed method can detect the insulation ageing condition with the error about 6%. There is no other method which can quantify the ageing degree of inter-turn insulation in the presence of groundwall insulation ageing. Compared with the black-box data-driven methods, the proposed method is based on a model with clear physical meaning, which makes the results more reliable.

Mixed Eccentricity Fault Detection for Induction Motors Based on Time Synchronous Averaging of Vibration Signals

This paper presents a method based on vibration signals to diagnose rotor mixed eccentricity faults in three-phase squirrel cage induction motors (SCIMs). Rotor eccentricity is a typical fault in SCIMs that significantly affects the performance of electrical machines and results in unwanted vibrations. The time synchronous averaging (TSA) signal, which consists of the harmonics components generated by the fault, is obtained via the characteristic frequency of the eccentricity fault. TSA is decomposed into two signals: TSA-regular and TSA-difference, which are respectively similar to approximate and details of wavelet transform. Based on the fault index, which is the maximum FFT value of the TSA-difference signal, the healthy and faulty motors can be distinguished. The proposed approach is evaluated on an experimental test rig equipped with a 1.5 kW SCIM supplied directly from the power grid under various load conditions. The results illustrate the effectiveness of the proposed approach in detecting the rotor mixed eccentricity faults.

Optimal drive cycle current supply of a wound field automotive electrical machine using surrogate models

Surrogate models have become a widely used solution for reducing computation times along design processes. In this work, a Gaussian Process surrogate model is built and used to predict the performance and losses of a wound field electrical machine in a fast manner. This approach is relevant, especially for drive cycle calculations that rapidly generate rising computation costs if they are computed using physical models, especially finite elements analysis. We present in detail the established method and a comparison of the obtained results with finite elements results. In addition, a detailed analysis of the optimized current supply is presented, and the advantages of variable excitation current are highlighted.

Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches

Abstract Optimisation, or optimal design, has become a fundamental aspect of engineering across various domains, including power devices, power systems, and industrial systems. Engineers and academics have been actively involved in optimising these systems to achieve better performance, efficiency, and cost-effectiveness. Optimising electrical machines, including permanent magnet motors, is a complex task. It often involves solving intricate problems with various parameters and constraints. Engineers use different optimisation methods to tackle these challenges. Depending on the specific requirements and goals of a design project, engineers may employ either single-objective or multi-objective optimisation approaches. Single-objective optimisation focuses on optimising a single objective, while multi-objective optimisation considers multiple conflicting objectives. In optimisation, objective functions are mathematical representations of what needs to be optimised. In this case, optimising the efficiency of the motor, reducing cogging torque, and minimising the total weight of active materials are defined as possible objective functions. Genetic algorithms are nature based algorithms that are commonly used in engineering to find optimal solutions to complex problems, including those with multiple objectives. In this paper, after conducting optimisations using different objective functions and methods, a comparative analysis of the results is performed. This helps in understanding the trade-offs and benefits of different design choices. Finite element analysis (FEA) is a computational method used to analyse the physical properties and behaviours of complex structures and systems. In this case, FEA is used to validate and analyse selected optimisation solutions to ensure they meet the desired characteristics and parameters. Overall, this work demonstrates the interdisciplinary nature of engineering, where mathematics, computer science (for optimisation algorithms), and physics (for FEA) converge to improve the performance and efficiency of electrical machines. It also underscores the importance of considering multiple objectives in design processes to find optimal solutions that strike a balance between competing goals.

A Review on the Effect of Electrical Steel Manufacturing Processes on the Performance of Electric Machines

The manufacturing processing of Electrical Steel Laminations (ESLs) for electric machines comprises cutting, stacking, and housing techniques which can result in plastic deformation and residual stress in the soft magnetic material. These manufacturing processes result in decreasing the magnetic quality and a local increase in both the static and dynamic hysteresis losses near the cut edges and consequently a reduction in the performance of the designed motor. The iron losses resulting from cutting can vary by a factor of two or more depending on geometrical, material, and processing parameters as well as the magnetic field strength. It is advantageous to consider these manufacturing effects in the design stage to reduce the probability of underperforming mass production. In this paper, the manufacturing processes of ESLs and their modelling methods are comprehensively surveyed. The gaps in scientific understanding and the research need for the expansion of accurate modelling of the cutting and joining of ESLs are subsequently discussed.

Vibration Characteristics and Vibration Mitigation Analysis of End Cap PM Excitation Homopolar Inductor Machine

Vibration analysis and vibration mitigation are very important to ensure the safe and stable operation of electrical machines applied in flywheel energy storage systems (FESS). In traditional integer slot synchronous machines, it is generally believed that there is only the zeroth-mode vibration caused by odd tooth harmonics. However, the homopolar inductor machine (HIM) adopts the end cap excitation structure, which makes its air-gap magnetic field contain the DC component, odd tooth harmonics and even tooth harmonics. The force density distribution on the slotted stator is analyzed by the magnetomotive force-multiplying permeance method. The results show that there are both zeroth- and pth(pole pairs)-mode vibrations caused by tooth harmonics and DC component of the air-gap flux density. Furthermore, to reduce vibration and resonance, and to improve the electromagnetic performance of HIM, it is best to use the stator skew slot, the magnetic slot wedge (MSW) and the sinusoidal rotor slot at the same time. Finally, a 54-slot/6-pole EPE-HIM is tested to validate the analytical and the finite-element method (FEM) results, and the variation trend of the experimental results and FEM results is the same.

Investigative study on the properties of magnetic materials for electrical machines

<p><span>The paper attempts to investigate the properties of the magnetic materials used in the construction of rotating electric machines, with a view to arrive at the appropriate choice for the induction motors in particular. Measurement of magnetic properties for rotating electrical machines are more important to both machine designer and operator. This paper discusses the influence of magnetic properties of electrical steel on performance of electrical machine. Here properties like BH curve, core loss, permeability and other vital parameters are measured using 400×400 Epstein tester over wide range flux density and frequency up to 50 Hz and 400 Hz. The results measured in terms of the core loss, BH curve and permeability showcase the benefits to decide on the suitability of the material for use in practice.</span></p>

Results of study of quantization and discretization error of digital tachometers with encoder

The analysis of measuring channels of angular velocity with an encoder given by the authors made it possible for the first time to obtain an equation for estimating the quantization and sampling error for an exponential mathematical model describing the transient process of operation of electrical machines. The components of the mathematical model of this dynamic error are the sampling step and the derivative, which characterizes the rate of change of the measured value over time. It was found that the errors of quantization and sampling significantly depend on the value of the resolution z of the encoder. Moreover, an increase in z leads to a decrease in the sampling error, but the relative quantization error increases. To reconcile these components of errors, the laws of change in the distinguishing ability z of the encoder are adaptive to the dynamic properties of the change in angular velocity over time.
 Proved that to ensure the maximum speed of measuring the angular velocity during the transient process, it is advisable to implement the method of changing the distinguishing ability of the encoder on the internal timers of the microcontroller proposed adaptive to its dynamic properties, and the quantization of informative periods proportional to the measured angular velocity should be carried out in "adjoining intervals".

Machining of Triangular Holes in D2 Steel by the Use of Non-Conventional Electrodes in Die-Sinking Electric Discharge Machining.

Electric discharge machining is relatively a slow process in terms of machining time and material removal rate. The presence of overcut and the hole taper angle caused by the excessive tool wear are other challenges in the electric discharge machining die-sinking process. The areas of focus to solve these challenges in the performance of electric discharge machines include increasing the rate of material removal, decreasing the rate of tool wear, and reducing the rate of hole taper angle and overcut. Triangular cross-sectional through-holes have been produced in D2 steel through die-sinking electric discharge machining (EDM). Conventionally, the electrode with uniform triangular cross-section throughout the electrode length is used to machine triangular holes. In this study, new designs of electrodes (non-conventional designs) are employed by introducing circular relief angles. For material removal rate (MRR), tool wear rate (TWR), overcut, taper angle, and surface roughness of the machined holes, the machining performance of conventional and unconventional electrode designs is compared. A significant improvement in MRR (32.6% increase) has been achieved by using non-conventional electrode designs. Similarly, the hole quality resulted by non-conventional electrodes is way better than hole quality corresponding to conventional electrode designs, especially in terms of overcut and hole taper angle. A reduction of 20.6% in overcut and a reduction of 72.5% in taper angle can be achieved through newly designed electrodes. Finally, one electrode design has been selected (electrode with 20 degree relief angle) as the most appropriate electrode resulting in better EDM performance in terms of MRR, TWR, overcut, taper angle, and surface roughness of triangular holes.

Investigation and Simulation of Technical Power Losses on 33/11kv Distribution Feeders Based on Gauss-Seidel Iterative Approach

Loss of technical performance is one of the main causes of energy shortages and inefficient operation of electrical machines in energy systems. In this study, an analysis of power losses for six selected 33/11 kV distribution feeders was presented. (Kakoba, Ntare, Karamurani, Ishaka MbararaNorth1 and Mbarara North 2) at Mbarara Central Substation Network of UMEME Electricity Distribution Company in Uganda. Data such as cable type, conductor resistance, conductor reactance, and route length were obtained from energy suppliers. From this data, the Gauss-Seidel method was used to determine the total active power and associated reactive power of the entire substation system. The findings showed that the substation is profitable due to the small calculated total power losses. Although it is possible to reduce the total loss to at least 9-12%. Losses were due to old substations, overloaded transformers, and copper losses core losses in lengthy feeder routes. To improve the performance of power distribution networks, it was recommended to replace old conductors to reduce losses.

Coupled Electromagnetic–Thermal Modelling of Dynamic Performance for Modular SPM Machines

This paper presents coupled electromagnetic (EM)–thermal modelling of the steady-state dynamic performances, such as torque speed curve and the efficiency map, for surface-mounted permanent magnet machines. One important feature of such a model is that it considers the demagnetization caused by magnet temperature rise at different rotor speeds. EM-only simulations, which often assume that the machines operate under constant temperature, have been widely used in the literature. However, the interaction between EM and thermal performances could lead to very different dynamic performance prediction. This is because the material properties, e.g., magnet remanence, coercivity, and copper resistivity are temperature-dependent. The temperature rise within electrical machines reduces torque/power density, PM eddy current losses, and iron losses but increases copper loss. Therefore, the coupled EM–thermal modelling is essential to determine accurate temperature variation and to obtain accurate EM performances of electrical machines. In this paper, the coupled EM–thermal modelling is implemented for both modular and non-modular machines to reveal the advantages of the modular machine under different operating conditions. The results show that the modular machine generally has better dynamic performance than the non-modular machine because the introduced flux gaps in alternate stator teeth can boost both EM and thermal performance.

A Meta-Learning Method for Electric Machine Bearing Fault Diagnosis Under Varying Working Conditions With Limited Data

Effective detection of fault in rolling bearings with a limited amount of data is essential for the safe operation of electric machines. This article proposes a novel meta-learning-enabled method for the detection of fault in rolling bearings of electric machines under varying working conditions with limited data. The fault diagnosis under various working conditions is cast as a few-shot classification problem, which is solved using a model-agnostic meta-learning-based model. Specifically, a meta-learner is first trained using a series of interrelated fault-diagnosis tasks under various working conditions. During this stage, the gradient-by-gradient rule is utilized for parameter optimization to achieve an effective representation of these tasks. Then, the parameters of the meta-learner are refined on a new task. This technique can achieve fast adaptation to new tasks by utilizing only few-shot samples. The proposed method can obtain high fault-detection accuracy under various working conditions when only a limited amount of data is available. Comparative tests among various methods were carried out on the Case Western Reserve University Bearing Dataset and the Paderborn University Rolling Bearing Dataset. The results show that the proposed model performs better than other state-of-the-art methods under various working conditions; our method has stronger generalization ability and faster adaptation ability. The fault diagnosis accuracy for both datasets was at least 99%, which proves that the proposed strategy can be flexibly applied to various scenarios.

Development of dual phase soft magnetic laminate for advanced electric machines

The development of dual phase soft magnetic laminate for advanced electric machines at General Electric is summarized, including alloy development, properties, manufacturing trials, prototype experience, potential applications, and future material development. Dual phase soft magnetic laminates provide the flexibility to tune magnetic/non-magnetic state at the desired locations, using solution nitriding treatment with protective mask/coating. This enabling material technology benefits electric machine performance by reducing flux leakage in the laminates. It also decouples the electromagnetic and mechanical design, which opens up the design space for advanced electric machines. Magnetic and mechanical properties of dual phase laminates were compared to commercial FeSi and FeCo soft magnetic laminates. Driven by the market needs for high power density and high-speed electric machines, a synchronous reluctance (SynRel) machine without PMs using dual phase soft magnetic laminates was assessed in machine electromagnetic performance as well as rotor stress analysis by finite element simulation. Successful prototype build and testing further retired manufacturing risks and advanced the technology maturity level. Future material development with higher mechanical strength, higher saturation magnetization, higher permeability, and lower loss are highly desired for ground and air transportation electrification.

Multi-Attribute Machine Learning Model for Electrical Motors Performance Prediction

Designing an electrical motor is a complex process that needs to deal with the non-linearity phenomena caused by the saturation of the iron at high magnetic field strength, the multi-physical nature of the investigated system and with requirements that may come into conflict. This paper proposes to use geometric parametric models to evaluate the multi-physical performances of electrical machines and build a machine learning model that is able to predict multi-physical characteristics of electrical machines from input geometrical parameters. The focus of this work is to accurately estimate the electromagnetic characteristics, motor losses and stator natural frequencies, using the developed machine learning model, at the early-design stage of the electrical motor, when the information about the housing is not available and to include the model in optimisation loops, to speed-up the computational time. Three individual machine learning models are built for each physics analysed, a model for the torque and back electromotive force harmonic orders, one model for motor losses and another one for natural frequencies of the mode-shapes. The necessary data is obtained by varying the geometrical parameters of 2D electromagnetic and 3D structural motor parametric models. The accuracy of different machine learning regression algorithms are compared to obtain the best model for each physics involved. Ultimately, the developed multi-attribute model is integrated in an optimisation routine, to compare the computational time with the classical finite element analysis (FEA) optimisation approach.

The Effect of Magnetic Flux Density Magnitude on Machine Performance

The effect of magnetic flux density magnitude on electromagnetic performance of a double stator permanent magnet synchronous machine is investigated and presented in this study. Finite element analysis method is applied in computing the results. The considered machine variables are: torque, losses, efficiency and electromagnetic power. The results reveal that output performances of a given machine would be affected by the magnitude of its magnetic flux density, particularly under saturation condition. Most promising electric machine characteristics are shown to be obtained from the device when the magnetic flux density magnitude is within 1.5 to 2.0 Tesla. The impact of this flux density amplitude should be taken into consideration during the design and operation of electrical machines, for effective and efficient output delivery. Highest amount of output torque, power and efficiency of 2.61 Nm, 109.46 W and 89.22 % are obtained from the simulated model at flux density amplitude of 1.6 Tesla.

Стенд для проведения приемо-сдаточных испытаний асинхронных электрических машин методом взаимной нагрузки

Improving the reliability and increasing the operational life of electrical equipment of electric rolling stock is one of the eight directions of scientific and technical development of JSC «Russian Railways» described in the target program «Strategy for the development of railway transport in the Russian Federation until 2030», approved by the decree of the Government of the Russian Federation. The solution to the problem of improving operational reliability is associated with comprehensive measures that include a wide range of issues aimed at ensuring the stable operation of rolling stock and its systems. Reliable operation of electric machines, which depends on the quality of repairs, has a significant impact on the operational reliability of electric rolling stock.