Dynamic Monitoring and Real-Time Data Acquisition Technology for Wire Status Based on Artificial Intelligence Timing Analysis

With the development of smart grids, the demand for wire condition monitoring is increasing. However, traditional monitoring methods face problems such as insufficient real-time performance, limited data analysis capabilities, and lack of intelligence. The data source of this paper is various sensors installed on-site on the main transmission lines in City A. The collection objects are mainly temperature, humidity, and wind speed, and the collection period is from January 2024 to June 2024. In the subsequent data processing and analysis process, the raw data undergoes preprocessing, including data cleaning, normalization, trend removal, and outlier handling, to ensure the accuracy and reliability of the data. This paper uses deep belief networks to capture feature information from wire state data through multi-layer nonlinear mapping and classify it. Additionally, a recurrent neural network is employed to dynamically predict wire state by dividing the input sequence into fixed-length subsequences. The results show that the average response time of the monitoring system based on deep belief networks is 182.7[Formula: see text]ms, which is 67[Formula: see text]ms shorter than traditional monitoring systems and significantly reduces the missed detection rate of potential faults. The intelligent wire condition monitoring system studied in this paper has improved the safety and reliability of power infrastructure.