摘要
传统检修方法不能满足配网设备的当下用网状态,导致低能耗运行现象的频繁发生。为解决上述问题,提出一种基于机器学习的新型配网设备状态自动检修方法。通过确定配网通信节点聚类方式的手段,对配网设备进行自适应连接及调试监测,完成基于机器学习的配网设备状态自适应调试。在此基础上,通过匹配检修语义、调整及确认已生成安措执行条目文件的方式,完成新型检修方法的搭建,实现基于机器学习的配网设备状态自动检修方法研究。模拟对比实验结果显示,与传统检修方法相比,应用新型自动检修方法后,配网设备当下用网状态满足率提升50%,造成低能耗运行发生几率降低30%。
Traditional maintenance methods can not meet the current network status of distribution network equipment,resulting in frequent low-energy operation phenomenon.In order to solve the above problems,a new automatic maintenance method based on machine learning is proposed.By means of determining the clustering mode of distribution network communication nodes,the distribution network equipment is self-adaptively connected and debugged and monitored,and the state of distribution network equipment is self-adaptively debugged based on machine learning.On this basis,by matching the maintenance semantics,adjusting and confirming the generated security execution entry files,a new maintenance method is built to realize the automatic maintenance method of distribution network equipment status based on machine learning.The simulation results show that compared with the traditional maintenance method,the new automatic maintenance method can increase the current network state satisfaction rate of distribution network equipment by 50%,and reduce the probability of low energy consumption operation by 30%.
作者
李互刚
LI Hugang(State grid ningxia power company shizuishan power supply company,Ningxia 753000,China)
出处
《自动化与仪器仪表》
2019年第10期148-152,共5页
Automation & Instrumentation
基金
国家自然科学基金资助项目(11871259)
关键词
机器学习
配网设备
自动检修
自适应调试
安措执行
machine learning
distribution network equipment
automatic maintenance
adaptive debugging
security measures
