Framework of Cable Intelligent Maintenance Based on Big Data Analysis

Abstract

For electric power companies, more and more data are stored in real-time databases through DAS and DCS systems. Using big data to analyze historical data can predict energy development trends and provide effective decision-making basis and future cable maintenance. work and maintenance etc. This paper proposes a risk assessment method for cable overhaul, that is, the estimated untreated risk is expressed by multiplying the post-treatment result by the failure rate. Comparing the results of an overhaul with the estimated risk of not overhauling provides the most decision-making results. This paper constructs a cable maintenance status evaluation management system based on big data, including the responsibility system, process system, control system and information management system of cable maintenance, in order to provide assistance for power development and increase the economic benefits of power companies. In this paper, the fundamentals of the mechanical properties of the elongation at break of cable fiber materials at different temperatures are investigated. Experimental studies show that the aging time and elongation at break of the XLPE sample cable at 140 °C to the critical reaction point are 26 days and 589%, respectively. The inflection point aging time is 13 days at 150 °C and 160 °C. Due to the effect of high-temperature aging, the mechanical properties of XLPE samples are severely damaged in a short period of time.