With the rapid increase of global population, the power load increases rapidly, and the electrical equipment such as generator, transformer, and power line in the power system is an important basis for production and life. Its safe operation is of great significance because it is related to people’s economic development and life stability. In recent years, with the rapid development of Internet of Things technology, the Internet of Things and electrical equipment safety early warning are combined to use their respective advantages to provide a new way for electrical equipment monitoring and early warning system. Through a variety of key technologies of the Internet of Things, such as sensor technology, network communication technology, and cloud computing technology, data, information exchange, and communication between electrical equipment and Internet technology are carried out according to the agreed protocol, and the safety of the operation status of electrical equipment is monitored in real time, so as to prevent power equipment failure and other problems. Based on this background, this paper studies the Internet of Things technology and electrical equipment monitoring and early warning system and analyzes its three-tier network architecture mode from the Internet of Things technology, namely, perception layer, network layer, and application layer. The combination of cloud computing and edge computing is studied and analyzed to provide theoretical support for the research of electrical equipment monitoring and safety early warning system. The wireless sensor network equipment is also installed on the electrical equipment through the Internet of Things technology to transmit data to the base station, so as to monitor whether the equipment operates safely. The monitoring and early warning system of wireless sensor system based on Internet of Things is given through case experiment. This system realizes relevant intelligent application services, which can not only ensure the stability of information transportation but also real-time monitoring of electrical equipment, early warning, shorten troubleshooting time, reduce the workload of power station staff, and achieve the functions of safety early warning, emergency command, and control. It is of great significance to the monitoring and early warning system of electrical equipment.

Identifying the main byproducts of SF₆ decomposition proves to be an effective strategy for determining the nature and severity of internal discharge faults in gas-insulated switchgears (GISs). In this research, it was suggested to utilize the coordination polymer Zr-MOF-808 as a sensor for the main byproducts of SF₆ decomposition. This study examined the adsorption of SF₆ and its main decomposition products (CF₄, CS₂, SO₂, SO₂F₂, and SOF₂) on Zr-MOF-808, utilizing Gaussian16 simulation software through a method anchored on quantum chemistry. Adsorption parameters were calculated and analyzed, including binding energy, charge transfer, adsorption distance, along with variations in bond length, bond angle, density of states, and frontier orbital of gas molecules. Our research indicated that the Zr-MOF-808 cluster demonstrated varying degrees of chemical adsorption for the six gases, leading to differential conductivity changes in each system following adsorption. Consequently, the detection of resistance value alterations in the materials would allow for the identification of the gas products.

With the rapid development of China’s electrical industry, the safe operation of electrical facilities is very important for social stability and people’s property safety. The failure detection method of conventional electrical equipment is hand detection, which has high experience of the detection person, lacks detection and error detection, and the detection efficiency is low. With the development of artificial intelligence technology, computer-assisted substation inspection is now possible, and substation inspection using an intelligent inspection robot equipped with an infrared device is one of the main substation inspection methods. In this paper, experiments are carried out using several neural network models. For example, if a faster region convolutional neural networks (RCNN) infrared detection model is employed, a good vg16 in the feature region of the extracted image takes into account the quality of the infrared image and the presence of multiple devices. Infrared images can be used to determine the basic features of various electronic devices. In order to detect targets in infrared images of electrical equipment, the fast RCNN target detection algorithm is used, and the overall recognition accuracy reaches 83.1%, and a good application effect is obtained.

SF₆ gas is an arc extinguishing medium that is widely used in gas insulated switchgear (GIS). When insulation failure occurs in GIS, it leads to the decomposition of SF₆ in partial discharge (PD) and other environments. The detection of the main decomposition components of SF₆ is an effective method to diagnose the type and degree of discharge fault. In this paper, Mg-MOF-74 is proposed as a gas sensing nanomaterial for detecting the main decomposition components of SF₆. The adsorption of SF₆, CF₄, CS₂, H₂S, SO₂, SO₂F₂ and SOF₂ on Mg-MOF-74 was calculated by Gaussian16 simulation software based on density functional theory. The analysis includes parameters of the adsorption process such as binding energy, charge transfer, and adsorption distance, as well as the change in bond length, bond angle, density of states, and frontier orbital of the gas molecules. The results show that Mg-MOF-74 has different degrees of adsorption for seven gases, and chemical adsorption will lead to changes in the conductivity of the system; therefore, it can be used as a gas sensing material for the preparation of SF6 decomposition component gas sensors.

The use of carbon nanotubes (CNTs) as the reinforcing phase to prepare copper-based composite materials can improve the strength and high conductivity of copper-based conductors. In order to analyze the effect of surface oxidation modification on the structural properties of carbon nanotubes and its strengthening effect on composite materials, this article combines heterogeneous copolymerization liquid phase mixing method and spark plasma sintering molding method; prepares the carbon nanotubes/copper composite materials using carbon nanotubes under different oxidation treatment conditions as the reinforcing phase (the volume fraction of carbon nanotubes is 3%); and characterizes the microstructure, mechanical properties, and electrical and thermal conductivity of the composite material. Studies have shown that the tensile strength and hardness of composite materials first increase with the increase of CNT oxidation treatment time and then decrease with the increase of oxidation treatment time. When the oxidation treatment time is 4 h, CNTs are uniformly dispersed in the matrix while maintaining good structural integrity and load-bearing capacity, and the composite material has the highest mechanical properties. The tensile strength of the composite material made of 80‐ nm CNTs reaches 452.4 MPa, which is 1.6 times that of pure copper, and the hardness reaches 127.4HV, which is twice that of pure copper. The electrical conductivity and thermal conductivity of the composite material first increase with the increase of the oxidation treatment time of carbon nanotubes and then decrease with the increase of the oxidation treatment time. The 80 nm CNT reinforced composite material has better CNT dispersion performance and higher conductivity than that of 15 nm CNT preparation. The electrical conductivity of the composite material reaches the maximum value of 92% IACS when the CNT oxidation treatment time is 4 hours, which is 95% of the pure copper sample, and the electrical conductivity is significantly better than that of the CNT/Cu composite material and copper alloy prepared by other methods. The thermal conductivity of composite materials is lower than that of pure copper. The thermal conductivity of carbon nanotubes with an oxidation treatment time of 2 h decreases most obviously, indicating that the thermal resistance generated by the interface and agglomeration phases in the composite material affects its thermal conductivity.