No abstract

With the continuous development of social economy, more and more attention is paid to the safety of power systems. However, since the power system involves a wide range of areas, how to effectively maintain power safety is extremely important. The AI image recognition technology is introduced to effectively identify the relevant signal lamps, digital instrument panels, switch positions, etc., of power equipment and sort out the specific identification process. Simulation experiments prove that AI image recognition is effective and can support the application of power systems.

With the continuous advancement of social economy, electric power, as an essential resource, plays an important supporting role for the development of industry and the lives of residents. As electric power itself is dangerous to a certain level, it is crucial to use the electric power rationally and ensure the safe application of electrical equipment. In this paper, AI image recognition technology is introduced to establish a comprehensive monitoring system for power transmission lines. Through the control of the potential risks of the transmission lines, emergency treatment of power transmission lines is implemented, which has provided an application support for the safe use of power equipment. Simulation experiments have verified that the image recognition technology based on AI can support omnidirectional monitoring of power transmission lines effectively.

The continuous expansion of the scale of power grid construction has led to the emergence and development of the uninterrupted operation of the unmanned aerial vehicle (UAV) distribution network. However, in the actual power production process, due to the limitations of operating conditions and environment, power safety accidents are inevitable. With the support of intelligent technology, the monitoring effect and application level of the safety monitoring system for the uninterrupted operation of the UAV distribution network have been improved. Unified planning and scheduling were carried out throughout the job implementation process. The efficiency of equipment safety management has been strengthened, which is of great practical significance to the safe production of electric power. Based on the analysis of the environmental characteristics and implementation difficulties of the unmanned aerial vehicle distribution network, this paper studied the safety monitoring system and method by combining the high intelligence and high-efficiency characteristics of the learning control robot technology. The designed unmanned aerial vehicle distribution network safety monitoring system and method were tested. In order to understand the effect of the system intuitively, this paper analyzed from three aspects: system sensitivity, anti-interference, and data acquisition and management. It also contrasted with traditional monitoring systems. The test data showed that the sensitivity test value of the traditional monitoring system in the R1 circuit with the lowest voltage intensity was 71.3%. In the R4 circuit with the strongest voltage intensity, the test value only reached 84.4%. The test values of the system in this paper in the two types of circuits were 85.6% and 93.3%, respectively. In the data acquisition and management test, the acquisition and management rates under the highest numerical information could reach more than 97%. It can be seen that it has strong feasibility in actual operation.