Magnetic components in power electronic equipment usually suffer from nonsinusoidal excitations. In this paper, a brief description about the existing core loss calculation methods for nonsinusoidal excitations is given. Then calculation formulas of aforementioned methods for two typical excitation waveforms, that is, square and rectangular with variable duty cycle ${D}$ , are derived. Core loss of nanocrystalline FT-3KS, under above two nonsinusoidal excitations, is measured on an established test setup. The accuracy and applicability of each method are discussed theoretically and verified experimentally, especially in terms of different duty cycles and frequencies. It is found that the waveform coefficient Steinmetz equation (WcSE) is more applicable for the case of relatively small harmonics content of magnetic field intensity ${H}$ (normal duty cycle region), while the improved generalized Steinmetz equation (IGSE) has better precision where the harmonics count (extreme duty cycle region). In addition, the IGSE can better follow the changing trend of core loss with ${D}$ and have better frequency applicability than WcSE, which is proven to be not valid in low frequency (where the static hysteresis loss dominates). The results can provide reference for core loss prediction under nonsinusoidal excitations.

Based on thermoelectric analogy method, a thermal network model coupled with electromagnetic loss is presented. According to the actual structure of dry-type transformer windings, an accurate thermal network is established. Considering the distribution characteristics of heat source along cooling ducts inside the dry-type transformer, the thermal parameters such as heat flux and convective heat transfer coefficients are calculated by finite element method analysis (FEM) and computational fluid dynamics (CFD). Based on temperature rise experiment, the hottest spot located at the top of outmost high voltage winding is 151.8 °C, which verified the correctness of thermal network model.

The foil winding temperature distribution is crucial to the life of power transformer since local heating can accelerate aging of insulation materials. However, based on the traditional 3-D electromagnetic model and the principle of heat transfer, the non-uniform losses and temperature distribution of foil windings are difficult to estimate because of complex magnetic leakage flux. In this paper, the magneto-fluid-thermal coupling model has been established to calculate the temperature rise and predict the potential hotspots of a simplified dry-type power transformer rated at 2500 kVA. Moreover, the influence of insulation barrier is considered to reshape the air-flow path and to affect the radiative effect between the high-voltage and low-voltage windings. Experimental temperature rises are measured with infrared thermography to verify the simulation results, which achieved reasonable hotspot predictions and provide the theoretical method for transformer designers.

The broad application of electronic device in the power systems requires a practical model to characterize hysteresis loops of electrical steel excited with harmonic components. However, the existing dynamic models may not appropriate for application because of their complexities and neglects of the minor loops caused by harmonics. This paper proposes a new algorithm that updates model parameters with magnetization history to modify Tellinen Model. The improved model can describe the static hysteresis loops containing minor loops and predict return-point memory effect. Also, combining with the Thin Sheet Model, a new dynamic model is built to characterize dynamic loops with harmonic components. The new model only requires quasi-static major hysteresis loops and dynamic major hysteresis loops under sinusoidal excitation to identify parameters. A comparison between measured loops and calculated results under both sinusoidal and harmonic excitations shows good agreement and validates the accuracy of the proposed model.

Vibration and acoustic noise are essential in electrical equipment. Amorphous alloy is widely used in the medium- to high-frequency transformer due to its low loss, but its significant vibration and noise cannot be ignored, which will even reduce the reliability of the equipment. Ultra-thin grain-oriented silicon steel (≤0.2 mm) is expected to design medium- to high-frequency transformers due to its high magnetic permeability, high saturation magnetic flux density, and high stability. In this article, the loss characteristic, three-dimensional vibration acceleration, and noise level of three C-type core samples made of ultra-thin grain-oriented silicon steels (GT-050, GT-100) and amorphous alloy CACC30 are measured and analyzed under sinusoidal and rectangular excitations. The mathematic analysis predicts that the vibration acceleration increases with duty cycle decrease under rectangular excitation, which is consistent with the experimental results. The ultra-thin grain-oriented silicon steel has lower vibration and noise under medium- to high-frequency excitation compared with amorphous alloy when the transformer is operated on the non-saturated state. The experimental results and analysis can promote the application of new high-efficiency magnetic materials in medium- to high-frequency electrical equipment.

The tactile perception of an object's surface material plays an important role in many intelligent fields including object recognition, robotic grasp, and environment exploration. Enlightened by the human active tactile perception process that originates from scanning a finger on the surface, a novel finger-like tactile sensor for surface material recognition has been designed according to the transduction mechanism of cilia and Villari effect of the iron-gallium alloy. Based on the structural dynamic theory of Euler-Bernoulli beam, the linear constitutive equations of magetostrictive material and the Faraday law of electromagnetic induction, the output voltage model of the sensor under vibration induced from the fingertip-surface scanning has been established. Furthermore, a prototype of the sensor, on the macroscale, is fabricated. In order to verify the performance, the sensor is used to measure induced vibration signals with texture properties during sliding on the surface and classify five similar texture surfaces with small differences with a classification algorithm. The experimental results indicate that the proposed tactile sensor is effective and highly accurate for surface material classification. The sensor is characterized by high sensitivity and quick response. It has the potential of being miniaturized and integrated into the finger of a robotic hand to realize surface material recognition in real time.