Intelligent Operation and Maintenance Approach for Distribution Rooms Based on Condition and Risk Assessment

This paper proposes an intelligent operation and maintenance approach for distribution rooms based on condition and risk assessment, aiming to address the issues of low efficiency, slow fault response, high maintenance costs, and the inability to promptly identify potential equipment faults in traditional distribution room operation and maintenance. The method employs Pearson correlation coefficients and Granger causality analysis to conduct correlation and causality analyses on equipment operation data, thereby identifying the key factors affecting equipment status. Subsequently, a time-series analysisbased equipment status prediction model is constructed to forecast the future status of equipment. The effectiveness of the intelligent operation and maintenance system is verified through actual case studies in conjunction with the architecture and functions of the intelligent operation and maintenance platform. The results demonstrate that intelligent operation and maintenance technology can significantly enhance the maintenance efficiency of distribution rooms and reduce fault risks and maintenance costs. The optimized operation and maintenance strategy leads to a noticeable decrease in the risk values of electrical equipment. This research strongly supports the modernization and intelligent management of distribution rooms, improves power systems' safety and economic benefits, and promotes technological progress in the power operation and maintenance industry.