Design Of Intelligent Monitoring And Early Warning System Against External Force Damage Of Transmission Line

Power resource is an important support for social and economic construction,which is closely related to social security development and economic benefits.The reliability of power supply is a strong support for social and economic development.Due to the layout of transmission lines need to cross a variety of different terrain,withstand different weather conditions,and even long-term operation in bad conditions,coupled with human activities and other natural disasters and many other possible factors,transmission lines are often damaged by external forces.In recent years,with the domestic construction of energy Internet and the widespread use of 4G and 5g network technology,the use of remote camera video monitoring of transmission lines in high-risk areas has become a very efficient and convenient method.By viewing the real-time data of the power grid background monitoring center,we can obtain the scene picture sent back by the camera,and monitor all kinds of external environment under the power grid,thus preventing the damage of external factors.However,the single human video monitoring is inefficient,and it can not ensure the timely detection of accidents.Based on the above problems,combined with my work experience,through the analysis of the characteristics of transmission line external force damage,an intelligent monitoring and early warning system for transmission line anti external force damage is constructed to improve the monitoring efficiency.The hardware and software design of the monitoring and early warning system is given,including video module,power supply module,radar module,alarm module and wireless transmission module.The video image of moving target is monitored,the feature quantity is extracted,the target trajectory is tracked and the intelligent training is carried out for learning.After comparing and evaluating multiple algorithms,the transfer learning algorithm is adopted.The algorithm is improved on faster r-cnn,and the effectiveness of transfer learning is verified by classifying cifar-10 data sets.The initialization parameter is set to vgg-16 model.The original samples collected in the work site are labeled,and the target monitoring data set which can be used for deep learning is established.After the system detects the intrusion target,it takes the way of on-site sound alarm and monitoring center alarm to drive the target,and notifies the grid staff to take corresponding measures to avoid the accident.After the installation and application in a power company in Chongqing,the test data show that the sensitivity of the technology can reach 6 meters,the system has high warning rate,low false alarm rate,rapid response,low power consumption and low flow consumption,which can effectively form an online warning response against external force damage,which is conducive to the scientific and efficient management of the transmission and distribution network.The popularization and application of the system will reduce the external force on the transmission line The possibility of damage to ensure the safe operation of the power grid.