基于深度学习的通信基站电源断路器运行状态监测方法

Operating Status Monitoring Method of Communication Base Station Power Circuit Breaker Based on Deep Learning

常规的电源用智能断路器运行状态监测模型多以目标驱动,监测效率较低,导致监测时延较大。因此,文章提出基于深度学习的通信基站电源断路器运行状态监测方法,实现实时监测点部署与监测数据预处理,提取断路器运行异常特征。采用自动化的形式,提高监测的效率,设计自动化电源用智能断路器运行状态监测模型,并采用动态追踪辨识的方式来完成状态监测。结果表明,设计的基于深度学习的通信基站电源断路器运行状态监测方法得出的监测时延较小,监测针对性较强。

Conventional intelligent circuit breaker operation status monitoring models for power supply are mostly target-driven,with low monitoring efficiency,resulting in large monitoring latency.Therefore,the article proposes a deep learning-based communication base station power circuit breaker operation status monitoring method,which is capable of real-time monitoring point deployment and monitoring data preprocessing,and extracts the abnormal characteristics of circuit breaker operation.Adopting the form of automation to improve the efficiency of monitoring,the intelligent circuit breaker operation state monitoring model for automated power supply is designed,and the dynamic tracking identification is used to complete the state monitoring.The results show that the designed deep learning-based communication base station power supply circuit breaker operation state monitoring method yields a smaller monitoring delay and more targeted monitoring.