This paper proposes a new surrogate optimization routine for optimal design of a direct on line (DOL) squirrel cage induction motor. The geometry of the motor is optimized to maximize its electromagnetic efficiency while respecting the constraints, such as output power and power factor. The routine uses the methodologies of Latin-hypercube sampling, a clustering technique and a Box–Behnken design for improving the accuracy of the surrogate model while efficiently utilizing the computational resources. The global search-based particle swarm optimization (PSO) algorithm is used for optimizing the surrogate model and the pattern search algorithm is used for fine-tuning the surrogate optimal solution. The proposed surrogate optimization routine achieved an optimal design with an electromagnetic efficiency of $93.90\%$, for a 7.5 kW motor. To benchmark the performance of the surrogate optimization routine, a comparative analysis was carried out with a direct optimization routine that uses a finite element method (FEM)-based machine model as a cost function.

In this article, we propose an analytical method to compute the eddy-current loss in nonlinear thick steel laminations (3–12 mm) by considering the return path of the eddy currents. Initially, a 2-D finite-element (FE) model is applied to segregate losses measured from toroidal material samples into hysteresis and eddy-current loss components to use them as reference. Afterward, a 2-D analytical time-domain model is proposed for the eddy currents based on the solution of the 2-D field problem. The time-domain model is then used to derive a simple frequency-domain eddy-current loss formulation for the sinusoidal flux density case with the inclusion of a skin-effect correction factor, which accounts for the nonlinearity of the material. Highly accurate results are obtained from the proposed model compared to FE reference results with a mean relative error of 5.1% in the nonlinear region.

Iron losses in laser-cut toroidal samples of 12 mm thick steel laminations used in large synchronous motors are studied. Eddy currents in the lamination cross-section are solved with the 2-D finite element method while applying a constitutive law based on the Jiles-Atherton hysteresis model. The effect of cutting on the material properties is included by a continuous local material model approach, which enables to express the material properties as a function of distance from the cutting edge. The accuracy of the model is validated by comparing the simulations and experimental measurements of five toroidal samples assembled from concentric rings with different widths. Highly accurate results are obtained in terms of both the matching of B-H loops and the total loss values with an average relative error less than 2.9%. The results show that the hysteresis loss under quasi-static excitation increases up to 20.4% due to the effect of cutting. It is observed that the eddy-current loss becomes dominant over the hysteresis loss even at 5 Hz, and this eddy-current loss decreases up to 72.5% as the number of concentric rings increases. The presented model and the results accurately show how iron losses occur in thick materials and how they are affected by the cutting process.

This paper presents an inverse thermal modeling technique to determine the core losses from the temperature rise inside the transformer core. For this purpose, initially, a customized printed circuit board (PCB) with thermal sensors is used to measure the temperature rise. Afterward, a 3D magneto-thermal forward model is developed to validate the temperature rise. The accuracy of the forward model is checked by comparing the simulated core losses and temperature rise of the transformer with experimental measurements for different supply conditions. The results show that the forward model can accurately estimate the core losses with a maximum relative error of less than 2.7% and predict the temperature rise in the core with a maximum relative error of less than 6.2%. Lastly, after ensuring the accuracy of the forward model, an inverse modeling technique is applied to the 3D thermal model to predict the core losses of the transformer directly from the measured temperature rise. The accuracy of the inverse model in estimating the core losses is checked by comparing the results with experimental measurements. The novel approach for the PCB design besides the inverse model shows that the technique can be applied to estimate the core losses directly from the measured temperature rise inside the core with a relative error of 2.7% compared to experiments.

This paper introduces a novel application of the inverse modelling method, which is designed to estimate core losses in both the stator and rotor regions of an electrical machine. The technique focuses on the use of short‐term transient temperature measurements obtained from the stator core of a slotless induction machine, with a focus on validating the measured temperature rise through a forward model. The measurement setup involves two primary approaches: (i) the use of thermal sensors embedded in a printed circuit board inside the stator core and (ii) surface sensors embedded on the stator yoke. Through the use of this innovative approach, the results indicate that the inverse modelling technique is highly effective in predicting core losses based on short‐time transient temperature rise measurements.

The knowledge of core losses in electrical machine is required for the successful design of highly efficient machines. This paper presents a thermographic technique for measuring core loss and localize loss density in an electrical machine. First, a 3D numerical solution of the stated problem was carried out in COMSOL multi-physics to study the possibility of the measurement technique. The experimental measurement process was demonstrated on a 24/12 V single-phase transformer. The excitation was supplied to the transformer by varying the applied voltage at a constant frequency of 300 Hz. IR camera was used to obtain the images of the core at the instant of supply for three different flux densities. The core losses were estimated from the initial rate of temperature rise and compared with the core loss obtained directly from the wattmeter. The results show that the developed system can accurately estimate core loss density and distribution over a wide range, with an error margin of less than 6%. However, the accuracy of the results obtained is affected by measurement condition.