机器人巡检升压站的电力异常诊断研究

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DOI：10.19981/j.CN23-1581/G3.2026.11.018

中图分类号：TP242

文献标志码：A

文章编号：2095-2945（2026）11-0080-04

Abstract： Aiming at the problems of low efficiency， high missed detection rate and high safety risks in traditional manual inspection of booster stations， this paper conducts research on power anomaly diagnosis in booster stations via robot inspection. By constructing an anomaly feature extraction model based on multi-sensor data fusion and combining improved deep learning algorithms， accurate diagnosis of power anomalies such as equipment temperature abnormalities， partial discharges and mechanical abnormal noises is realized. Experimental results show that the proposed method achieves an anomaly diagnosis accuracy of 96.8%， a false alarm rate as low as 2.1%， and the diagnosis time is shortened by 65% compared with traditional methods. It can effectively improve the intelligent level and reliability of booster station operation and maintenance， providing strong support for the safe and stable operation of power systems.

Keywords： robot inspection; booster station; power anomaly diagnosis; multi-sensor fusion; deep learning

升压站作为电力系统输变电环节的关键枢纽，其设备运行状态直接影响电网的安全稳定。（剩余5439字）