The combination of energetic material (EM) and electrical wire explosion (EWE) is an efficient and economical method to generate strong shock waves (SWs). The idea resembles the exploding bridge-wire (EBW) detonator, but in this case, the EWE operates with much higher electrical energy storage, and the EMs of much lower sensitivity can be directly ignited, which provides a practical compromise among limited energy storage, strong SWs, and safe usage of explosives. The hybrid load based on EWE ignited EMs has been successfully applied for reservoir stimulation in exploitation of both conventional and unconventional oil/gas resources. In this brief review, we summarize the EWE characteristics, SW characteristics, ignition mechanism, and engineering applications of this hybrid load, and give suggestions for future research directions.

The temperature rise test for transformers is time-consuming, energy-intensive, and has low detection efficiency. To improve the efficiency of the temperature rise test and reduce energy consumption, this paper proposes a temperature rise prediction method for oil-immersed transformer windings. This method is based on identifying the parameters of a thermal circuit model. Firstly, a fifth-order thermal circuit model of oil-immersed transformers is put forward. Then, based on a two-hour temperature rise curve, the thermal capacity and resistance model is identified through genetic algorithms. The obtained parameters are used to compute the temperature rise curve, steady-state average temperature rise, and top oil temperature rise. The results show that the heat capacities of the low-voltage (LV) winding, high-voltage (HV) winding, oil tank, and oil of a 400 kVA transformer are approximately 50 kJ/K, 75 kJ/K, 320 kJ/K, and 90 kJ/K, respectively. Additionally, the thermal resistances from the LV winding to oil, HV winding to oil, oil tank, and air are about 8 mK/W, 5 mK/W, 1 mK/W, and 11 mK/W, respectively. When the transformer capacity increases, the heating power of the windings escalates, and the oil resistance of HV windings decreases from 8 mK/W for a 400 kVA capacity to 5 mK/W for an 800 kVA capacity. The absolute prediction error for transformers of 400 kVA, 630 kVA, and 800 kVA is 2.9 °C. These findings can facilitate the swift detection and assessment of the winding temperature rise in oil-immersed transformers.

Distribution transformers serve as critical nodes in smart grids, and management of their recycling plays a vital role in the full life-cycle management for electrical equipment. However, the traditional manual dismantling methods often exhibit a low metal recovery efficiency and high levels of hazardous substance residue. To facilitate green, cost-effective, and fine-grained recycling of distribution transformers, this study proposes a fine-grained dismantling decision-making system based on a knowledge graph subgraph comparison and multimodal fusion perception. First, a standardized dismantling process is designed to achieve refined transformer decomposition. Second, a comprehensive set of multi-dimensional evaluation metrics is established to assess the effectiveness of various recycling strategies for different transformers. Finally, through the integration of multimodal perception with knowledge graph technology, the system achieves automated sequencing of the dismantling operations. The experimental results demonstrate that the proposed method attains 99% accuracy in identifying recyclable transformers and 97% accuracy in auction-based pricing. The residual oil rate in dismantled transformers is reduced to below 1%, while the metal recovery efficiency increases by 40%. Furthermore, the environmental sustainability and economic value are improved by 23% and 40%, respectively. This approach significantly enhances the recycling value and environmental safety of distribution transformers, providing effective technical support for smart grid development and environmental protection.

This study aims to develop an efficient recovery solution for waste transformer insulating oil, addressing the challenge of incomplete separation of residual oil in existing recovery technologies. A multi-module integrated system is constructed, comprising a waste oil extraction module, a residual oil vaporization module, an exhaust gas treatment module, and an online monitoring module. By combining steps such as oil extraction, residual oil absorption, hot air circulation heating, and negative-pressure low-frequency induction heating, the complete recovery of waste oil is achieved. The recovery process incorporates oil–gas saturation monitoring and an oil–gas precipitation assessment algorithm based on neural networks to enable intelligent control, ensuring thorough recovery of residual oil from transformers. The proposed system and methods demonstrate excellent recovery efficiency and environmental protection effects during the pre-treatment of waste transformer oil. Experiments conducted on 50 discarded transformers showed an average recovery efficiency exceeding 99%, with 49 transformers exhibiting no damage to core components after the recovery process. From a theoretical perspective, this research introduces monitoring and control methods for transformer insulating oil recovery, providing significant support for the green processing and reutilization of discarded transformer insulating oil. From an application value perspective, the recovery process helps reduce environmental pollution and facilitates the disassembly of transformers. This enables better analysis of transformer operating characteristics, thereby enhancing the reliability and safety of power systems.

As power system equipment gradually ages, the automated disassembly of transformers has become a critical area of research to enhance both efficiency and safety. This paper presents a transformer disassembly system designed for power systems, leveraging multimodal perception and collaborative processing. By integrating 2D images and 3D point cloud data captured by RGB-D cameras, the system enables the precise recognition and efficient disassembly of transformer covers and internal components through multimodal data fusion, deep learning models, and control technologies. The system employs an enhanced YOLOv8 model for positioning and identifying screw-fastened covers while also utilizing the STDC network for segmentation and cutting path planning of welded covers. In addition, the system captures 3D point cloud data of the transformer’s interior using multi-view RGB-D cameras and performs multimodal semantic segmentation and object detection via the ODIN model, facilitating the high-precision identification and cutting of complex components such as windings, studs, and silicon steel sheets. Experimental results show that the system achieves a recognition accuracy of 99% for both cover and internal component disassembly, with a disassembly success rate of 98%, demonstrating its high adaptability and safety in complex industrial environments.