Partial discharge (PD) is a widely adopted method for internal insulation detection of electrical transformers. The impact of the refraction and diffraction of the ultrasonic signal in the oil, winding and core in the transformer makes the localization of the partial discharge source inside the transformer a non-trivial task. The refraction and diffraction factors can introduce additional complexity to the localization equations that can be hardly solved. This paper proposes an algorithmic solution based on the time difference of arrival (TDOA) algorithm and semidefinite relaxation convex optimization to improve the PD source localization accuracy. The refraction, diffraction error, measurement error and their relationship in the process of PD signal propagation are fully considered. The proposed solution is extensively assessed through simulation, testbed and field experiments and the results confirm that the proposed solution outperforms the CHAN and particle swarm optimization (PSO) algorithm with the localization error of about 0.1m.

Topological metamaterial has been a research hotpot in both physics and engineering due to its unique ability of wave manipulation. The topological interface state, which can efficiently and robustly centralize the elastic wave energy, is promising to attain high-performance energy harvesting. Since most of environmental vibration energy is in low frequency range, the interface state is required to be designed at subwavelength range. To this end, this paper developed a topological metamaterial beam with local resonators and studied its energy-harvesting performance. First, the unit cell of this topological metamaterial beam consists of a host beam with two pairs of parasitic beams with tip mass. Then, the band structure and topological features are determined. It is revealed that by tuning the distance between these two pairs of parasitic beams, band inversion where topological features inverse can be obtained. Then, two sub-chains, their design based on two topologically distinct unit cells, are assembled together with a piezoelectric transducer placed at the conjunction, yielding the locally resonant, topological, metamaterial, beam-based piezoelectric energy harvester. After that, its transmittance property and output power were obtained by using the frequency domain analysis of COMSOL Multiphysics. It is clear that the subwavelength interface state is obtained at the band-folding bandgap. Meanwhile, in the interface state, elastic wave energy is successfully centralized at the conjunction. From the response distribution, it is found that the maximum response takes place on the parasitic beam rather than the host beam. Therefore, the piezoelectric transducer is recommended to be placed on the parasitic beam rather than host beam. Finally, the robustness of the topological interface state and its potential advantages on energy harvesting were studied by introducing a local defect. It is clear that in the interface state, the maximum response is always located at the conjunction regardless of the defect degree and location. In other words, the piezoelectric transducer placed at the conjunction can maintain a stable and high-efficiency output power in the interface state, which makes the whole system very reliable in practical implementation.

It is a key challenge to continuously power personal wearable health monitoring systems. This paper reports a novel liquid metal-enhanced wearable thermoelectric generator (LM-WTEG that directly converts body heat into electricity for powering the wearable sensor system. The gallium-based liquid metal alloys with room-temperature melting point (24~30 °C) and high latent heat density (about 500 MJ/m³) are used to design a new flexible finned heat sink, which not only absorbs the heat through the solid-liquid phase change of the LM and enhances the heat release to the ambient air due to its high thermal conduction. The LM finned is integrated with WTEG to present high biaxial flexibility, which could be tightly in contact with the skin. The LM-WTEG could achieve a super high output power density of 275 μW/cm² for the simulated heat source (37 °C) with the natural convective heat transfer condition. The energy management unit, the multi-parameter sensors (including temperature, humidity, and accelerometer), and Bluetooth module with a total energy consumption of about 65 μW are designed, which are fully powered from LM-WTEG through harvesting body heat.

With the continuous development of the Internet of Things technology, the Internet of Things technology can be used in many aspects of the smart grid. Apply advanced Internet of Things technologies in substation GIS, including sensing technology, network communication technology, intelligent decision-making technology, automatic control technology, and energy and power technology, etc., and combine them closely to form a coordinated organic system. At present, partial discharge is the main cause of equipment insulation deterioration in substations. Partial discharge detection and evaluation has become an important means of monitoring insulation conditions. The monitoring of GIS partial discharge conditions in substations can help find insulation defects in GIS in time, avoid insulation failures, and improve the safe operation of GIS. It is possible to achieve GIS insulation condition maintenance, reduce power outage time and save maintenance costs. The effective application of the Internet of Things technology in the GIS partial discharge state detection system of intelligent substations can effectively improve the construction of modern intelligent substations in China.

Corridor management—such as reliance on manual warning zone delineation and inconsistent boundaries—this paper proposes 3DSim-WZD, an automatic ground-level warning zone detection method based on 3D simulation data augmentation. Guided by "interpretable geometric priors combined with deep learning regression," the framework integrates four modules: parametric simulation generation, simulation-to-real transfer, boundary vertex regression, and voltage-level-based expansion. Specifically, parametric virtual scenes are constructed in Unity3D to automatically derive accurate vertex labels. The open-source Stable Diffusion framework, combined with ControlNet and LoRA, is employed for sim-to-real style transfer to reduce domain gaps. Furthermore, directional detection convolutional kernels are incorporated into the YOLO12m backbone to enhance sensitivity to transmission structures. Finally, safety clearance distances are mapped according to voltage levels for regulatory-compliant warning zones. Evaluated on a dataset of 5,000 simulated and 300 real samples, the method achieves a mIoU of 91.2% and an inference speed of 46.8 FPS, demonstrating significant potential for large-scale deployment.