Tinghui Li

Partial discharge (PD) is one of the most important factors affecting the stable operation of high-voltage equipment and poses a great threat to the reliability and safety of power systems. Although some deep learning-based methods, such as Convolutional Neural Networks (CNN), have been proposed for PD detection, there are still some shortcomings in terms of global dependencies. In this paper, it is proposed to develop a model of Convolutional Neural Networks-Gated Recurrent Unit-Multi-Head Attention (CNN-GRU-MHA). Wavelet transformation is adopted for noise suppression. reduction, together with statistical feature downsampling for reducing data dimensionality. In the model, CNN extract localized temporal features, GRU capture sequential temporal patterns, and MHA focuses on modeling global dependencies while highlighting critical features. Experimental evaluations reveal that the model reaches a Matthews correlation coefficient of 74.78% and a recall rate of 74.61%, performing better than some conventional methods in terms of both accuracy and robustness. This model securely and effectively provided a method of enhancing power system safety and stability.

Regulating spin-related electronic structures of two dimensional (2D) materials by an external electric field plays a substantial role in achieving spintronic and multistate information storage. However, electric-field-dependent ferromagnetic behavior at atomic-thick 2D materials is very difficult to be realized due to their intrinsic inversion symmetry, in which the symmetric spatial distribution of charge density makes it become insensitive to spontaneous polarization from external electric field. Herein, a new-type Janus MnReX₃ (X = Se, S) monolayer with noncentrosymmetric configuration in which their orbital hybridization at internal interface can be engineered by rearranging the spatial symmetry of out-of-plane charge density. As a result, the spin exchange interaction among magnetic sites can be regulated by the electric-field-driven charge density redistribution, leading to a controllable ferromagnetic behavior at room temperature. Our results not only suggest a promising strategy to regulate the ferromagnetic response by reducing the crystal symmetry, but also provide a new insight into designing 2D magnetic materials.

Electrochemical reduction of nitrate to ammonia faces the competition of multiple reactions. Herein, we focus on the spin-related reconfiguration of diatomic catalysts via their super-exchange interaction to enhance the reduction performance.