基于多尺度用电特征融合的低压双路电源同供隐患研判算法OA

Low-voltage dual-power supply simultaneously is a common abnormal power safety hazard on the low-voltage side.Due to the failure of the dual-power supply switch to be installed or malfunctioning,the two distribution transformers are directly connected on the low-voltage side.In serious cases,it may cause line fires,electric shock injuries and deaths.It is highly concealed and difficult to detect,and it is impossible to detect problems in a timely and comprehensive manner by relying solely on on-site inspections by operation and maintenance personnel.This paper proposes an intelligent algorithm for identifying hidden dangers of low-voltage dual-power supply simultaneously based on multi-scale electricity consumption features fusion.It extracts three key features of statistical scale,time scale and spatial scale from electricity consumption current,power factor and other data,and then builds a co-supply hidden danger identification model called DangerSense based on multi-scale electricity consumption features fusion.It uses electric power big data analysis and artificial intelligence algorithm to directly locate safety hazard points,transforming traditional manual door-to-door inspections into data-driven intelligent identification,greatly improving the efficiency of problem verification by operation and maintenance personnel.Through case analysis,the average recognition Fl score of DangerSense model proposed in this paper reaches 90.52％.Compared with multiple benchmark algorithms,the method proposed in this paper has achieved significant improvement and has strong practical value.