The constant switching frequency space vector pulse width modulation (CSFSVPWM) generates high-frequency harmonics with integer multiples of the switching frequency, making it fail to fully exploit the potential of pulse width modulation (PWM) technique. In this paper, a variable switching frequency space vector pulse width modulation technique with the function of torque ripple prediction is proposed for induction motor drive system, which could optimize the high-frequency harmonic current distribution and torque ripple. The mathematical correlation between inverter average voltage and instantaneous voltage under the space vector pulse width modulation (SVPWM) is first analyzed, followed by the analysis of current ripple in a switching cycle using the typical seven-segment SVPWM, thus the torque ripple of induction motor during this period can be predicted. The switching period is dynamically adjusted according to the magnitude of predicted torque ripple, and the average switching frequency is maintained to be the same with that of CSFSVPWM. The effectiveness of the proposed method in suppressing high-frequency current harmonics and torque ripple is fully verified by experimenting on a 2.2 kW induction motor drive system, and a comprehensive comparison with CSFSVPWM and random PWM modulation is implemented.

Nanofluid is an innovative approach to improve the thermal conductivity of fluid by adding nanoparticles. Transformer oil-based nanofluids have been prepared and measured, which verifies the concept and demonstrates the potential in oil-immersed power transformer applications. In this paper, the effect that nanofluids (oil/SiC) have on the natural convective heat transfer in disc-type transformer windings is investigated numerically. The multi-phase mixture model is employed for the first time to analyze such a nanofluid flow field, and the single-phase model is also used for comparison and mutual authentication. After using the nanofluid, significant temperature drops inside the windings are observed, and in some cases, the heat transfer performance is further improved by the adjusted mass flow rate distribution. The impact of nanofluids is enhanced with the rising volume fraction of nanoparticles. Before performing the study, the numerical model is validated using the existing results of the referenced windings cooled by transformer oil.

Artificial intelligence and deep learning have been widely used in recent years to explore the possibility of accelerating computation. However, it has very few applications in magnetic field calculation. This article employs a conditional generative adversarial network (cGAN) to approximate the magnetic field of a coaxial magnetic gear and calculate the magnetic torque through postprocessing. The working principle of magnetic field approximation using cGAN and the training process is introduced in this study. We adopted conditional image-to-image translation technology in cGAN and compared different loss functions and residual structures combinations. Then, we found the best combination, which can accelerate convergence, reduce errors, and improve the generator's performance. Numerical experiments have verified the effectiveness of the proposed cGAN, and the average numerical error can be as small as 1%. At the same time, its speedup ratio is as high as 200 compared to the finite element method.

The demand for fast magnetic field approximation for the optimal design of electromagnetic devices is urgent nowadays. However, due to the lack of a publicly available dataset and the unclear definition of each parameter in the magnetic field dataset, the expansion of data-driven magnetic field approximation is severely limited. This study presents a physics-informed generative adversarial network (PIGAN), as well as a permanent magnet linear synchronous motor (PMLSM)-based magnetic field dataset, for fast magnetic field approximation. It includes the current density, material distribution, electromagnetic material properties, and other parameters of the electric machine. Physics-informed loss functions are utilized in the training process, making the output governed by Maxwell’s equation. Different slot-pole combinations of the PMLSM are involved in the dataset to extend the generalization of PIGAN. Some indicators for the further evaluation of magnetic approximation performance, including image-based metrics and calculation methods for the performance of electric motors, are presented in this study. Some challenges of magnetic field approximation using PIGAN are also discussed. The effectiveness of the physics-informed method is verified by comparing the magnetic field approximation results and the performance analysis results of the PMLSM with FEM, and the speed of PIGAN is approximately 40 times faster than that of FEM, while the accuracy is similar.

Dispersing nanoparticles with high thermal conductivity into transformer oil is an innovative approach to improve the thermal performance of traditional oil-immersed transformers. This mixture, also known as nanofluid, has shown the potential in practical application through experimental measurements. This paper presents the comparisons of nanofluid filled transformer and traditional oil-immersed transformer in terms of their computational fluid dynamics (CFD) solutions from the perspective of optimal design. Thermal performance of transformers with the same parameters except coolants is compared. A further comparison on heat transfer then is made after minimizing the oil volume and maximum temperature-rise of these two transformers. Adaptive multi-objective optimization method is employed to tackle this optimization problem.

In this paper, the response surface method (RSM) is exploited in the cooling system design optimization of oil-immersed transformers. To generate the training data for RSM, a 3-D computational fluid dynamic model for the transformer under study is constructed and solved, followed by verification on the solutions. Based on the simulation data, two types of RSMs, i.e., the second-order polynomial method and the Kriging method, are employed to evaluate their performance for this specific problem. The response surface with the best performance is used in the optimization process. The solution is compared with that found by the direct optimization method to showcase the effectiveness of applying RSM to oil-immersed transformer cooling system design. Details of implementation of the studied transformer are also presented.