During the past decades, the potential effects of extremely low-frequency electromagnetic fields (ELF-EMFs) on human health have gained great interest all around the world. Though the International Commission on Non-Ionizing Radiation Protection recommended a 100 μT, and then a 200 μT magnetic field limit, the long-term effects of ELF-EMFs on organisms and systems need to be further investigated. It was reported that both electrotherapy and possible effects on human health could be induced under ELF-EM radiation with varied EM frequencies and fields. This present article intends to systematically review the in vivo experimental outcome and the corresponding mechanisms to shed some light on the safety considerations of ELF-EMFs. This will further advance the subsequent application of electrotherapy in human health.

This study presents a novel approach for the modelling of transformer core magnetisation characteristics under DC bias condition by using the inverse Jiles–Atherton (J–A) model. An improved shuffled frog leaping algorithm (SFLA) is proposed to identify the five parameters of J–A model, where an adaptive chaotic mutation operation is added in the global searching process to increase the identification accuracy. With the proposed algorithm, the J–A model parameters under different DC components are identified based on the DC‐bias experiment on the real transformer. The conventional SFLA and particle swarm optimisation (PSO) method are also applied to identify the parameters of J–A model. All the identified results are compared with the measured B – H curves to verify their identification accuracy. Moreover, the least square support vector machine (LSSVM) algorithm is used to predict the J–A model parameters of transformer under larger DC component from the previously identified parameters in smaller DC. The calculated results have shown that the improved SFLA has higher identification accuracy than the conventional SFLA and PSO methods. Furthermore, LSSVM algorithm can effectively forecast the transformer magnetisation character under large DC bias condition, which is beneficial for the research of transformer DC bias problem.

Electric arc furnaces (EAFs) represent one of the most disturbing loads in the subtransmission or transmission electric power systems. Therefore, it is necessary to build a practical model to described the behavior of EAF in the simulation of power system for power quality issues. This EAF operation, the seemingly random arc paper deals with the modeling of EAF based on the combination of extended Kalman filter to identify the parameter of arc current and the power balance equation to obtain the dynamic, multi-valued v - i characteristics of EAF load. The whole EAF systems are simulated by means of power system blockset in MATLAB to validate the proposed EAF model. This model can also be used to assess the impact of the new plant or highly varying nonlinear loads that exhibit chaos in power systems

Tip discharge in oil-immersed transformers poses a significant threat to insulation integrity. Conventional detection methods, such as gas and electrical analysis, are limited by slow response times or susceptibility to interference. Additionally, the lack of systematic comparisons between aged and fresh oil using multi-modal signal correlations hinders the development of accurate diagnostic strategies. To address this, a multi-modal sensing platform employing optical, UHF, and HFCT sensors, complemented by visual observation, was developed to investigate the evolution characteristics and mechanisms of tip discharge and to compare the detection effectiveness of these methods. Experimental results reveal that aged oil undergoes a novel four-stage evolution, where discharge signals first rise to a local peak, then experience suppression, followed by a dramatic surge, and finally decline slightly before breakdown. This process is governed by an “Impurity-Assisted Cumulative Breakdown Mechanism,” driven by impurity bridge growth and space charge effects, with signal transitions from ‘decoupling’ to synchronization. The optical sensor demonstrated superior sensitivity in early discharge stages compared to electrical methods. In contrast, fresh oil exhibited a “High-Field-Driven Stochastic Breakdown Mechanism,” with isolated pulses from micro-bubble discharges maintaining a metastable state until a critical threshold triggers instantaneous failure. This study enhances the understanding of how oil condition alters discharge mechanisms and underscores the value of multi-modal sensing for insulation condition assessment.

Accurate assessment of winding condition for power transformers is critical for ensuring the stable operation of modern power systems. Vibration signal has been regarded as an effective and promising evaluator for winding diagnosis. While on-line vibration monitoring offers the continuous, non-invasive and in-service assessment for winding condition, establishing precise correlations between the variable vibration patterns and specific winding condition remains challenging. To this end, an off-line vibration response analysis (VRA) technique was presented in the paper. Specifically, vibration frequency response (VFR) curves, indicating the winding response, were first obtained when the transformer was excited by the developed vibration response testing system, consisting of constant current variable-frequency power supply, intermediate transformer, accelerometers, data acquisition, control and analysis system. The VFR curves were then quantitatively and comprehensively described through four kinds of correlation indices. Finally, hierarchical integration strategy was proposed to aggregate those indices into quantitative criterion for condition assessment. The proposed method was validated on a real transformer under both normal and fault conditions, demonstrating superior performance. Notably, a 10% decrease in the evaluation criterion indicates an incipient winding looseness, while a reduction of 25% or more suggests severe looseness, prompting timely maintenance recommendations.