一种高压电力电缆状态实时智能监测系统
By acquiring signals across the entire monitoring segment and implementing dynamic early warning adjustments, the limitations of existing technologies in monitoring partial discharge in cables and the inadequacy of type identification have been resolved. This enables real-time, accurate monitoring and preventative maintenance of high-voltage power cables, improving the accuracy and reliability of cable condition assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHONGBANG CABLE GRP CO LTD
- Filing Date
- 2026-03-30
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies only monitor partial discharge at cable joints, failing to achieve signal acquisition and interference elimination across the entire cable monitoring section. Furthermore, they do not perform type verification based on discharge location, resulting in limited monitoring range, insufficient accuracy in discharge type identification, inability to adapt to operating characteristics under different working conditions, lack of continuous assessment of cable insulation health status, and inability to achieve preventive maintenance.
The system employs a discharge signal acquisition module to collect signals across the entire monitoring section and eliminate electromagnetic interference. It also combines historical pulse propagation delay records of the cable to identify the location and type of discharge anomalies, dynamically adjusts early warning reference values, and assesses the insulation health status in conjunction with the cable's operating conditions, thereby achieving full-process monitoring and adaptive early warning.
It enables accurate acquisition and synchronous positioning of partial discharge signals across the entire cable monitoring section, improving the accuracy of discharge anomaly identification and the reliability of early warning. It can also capture the slow deterioration trend of cable insulation and reduce the probability of fault occurrence.
Smart Images
Figure CN121955618B_ABST