Research On The Quality And Efficiency Improvement Of Transmission Line Operation And Maintenance Based On Machine Learning

Transmission lines are an important part of the power network,connecting the supply side of electricity production and the demand side of electricity consumption,and also the main channel of national power transmission.The operation and maintenance of the transmission line is directly related to the safe and stable operation of the power grid,and determines whether the electric energy can be sent economically and reliably.With the continuous and rapid increase of the number of lines put into operation,the burden of operation and maintenance personnel is getting heavier and heavier.How to use modern information technology to effectively improve the quality and efficiency of transmission line operation and maintenance has become an important problem to be solved urgently.This thesis firstly introduces the transmission line and its operation and maintenance work.Combining with the quality control policy of line operation and maintenance project,it explains the meaning of transmission lines operation and maintenance quality and efficiency,and specifically analyzes six dimensions to measure operation and maintenance quality,namely comprehensiveness,advancement,stability,security,control and accuracy.It is pointed out that improving speed,reducing cost and scientific management are the main directions of improving operation and maintenance efficiency.The four factors causing the risk of line operation and maintenance are sorted out,and the risk management principles,processes and main prediction methods are clarified.On the basis of defining the concept and types of machine learning,the application value of machine learning in the field of intelligent risk management is analyzed,and the generalization method and application process of machine learning are described.Then,taking the line icing warning as an example,this thesis systematically analyzes how to improve the operation and maintenance quality of transmission lines in winter icing conditions based on machine learning.Use professional knowledge to analyze the types,hazards and causes of line icing,prepare and clean historical data,apply machine learning to build a model for predicting line icing conditions,and create scorecards through variable binning methods to divide early warnings according to scores level,formulate an early warning mechanism for line icing,and explain the dimensions and effects of improving the quality of operation and maintenance in the example.Next,this thesis takes the autonomous inspection optimization of UAVs as an example to explain in detail how to improve the operation and maintenance efficiency of transmission lines in inspection work based on machine learning.It is found that there are several difficulties in autonomous inspection of UAVs.Based on these,it is proposed to optimize the two aspects of “take-off and landing point selection” and “flight path”,apply machine learning to build models,and implement optimization strategies by using improved ant colony algorithm,the direction and effect of improving the operation and maintenance efficiency are described in the embodiment.Finally,from the three perspectives of equipment operation and maintenance,channel operation and maintenance and operation and maintenance management level,combined with the meaning of transmission line operation and maintenance quality and efficiency,it analyzes the ways to improve the quality and efficiency of operation and maintenance,and proposes two specific methods of applying machine learning suggestion.This thesis provides a solution based on machine learning for the improvement of the quality and efficiency of transmission line operation and maintenance,which is conducive to promoting the deepening of the development of big data technology in the field of transmission line operation and maintenance management,and also has a certain reference value in enterprise risk management.