Research On Condition Assessment And Fault Prediction Of Overhead Transmission Lines Based On Big Data Analysis

Overhead transmission lines are the weakest link in power network as a result of operating in complex environment for a long time,but traditional risk assessment model of transmission lines is difficult to be used in complex environment,which brings challenges to transmission lines condition assessment and fault prediction.With the accumulation of transmission lines data and the development of big data technology,the technology of big data is gradually dominant in equipment condition assessment and fault prediction.In this paper,the following research work is carried out for overhead transmission lines condition assessment and fault prediction:A condition assessment method of overhead transmission lines based on association rules and belief networks is proposed.A database of transmission lines state association rules is constructed based on Hotspot association algorithm,which combines transmission lines status data and meteorological data in actual operation environment.A bayesian network for overhead transmission lines state prediction is constructed based on historical state data,so the state prediction model of transmission lines is constructed.The prediction results are compared with the actual operation data of GZ power grid from 2016 to 2018,which shows the validity of the method.A lightning risk assessment method of overhead transmission lines based on multidimensional correlation information fusion is proposed.The relationship between line ontology characteristic factor and environment characteristic factor and lightning strike fault accident of overhead transmission line is analyzed by association rules.Multi-source association information fusion is realized based on Information Entropy Theory,Grey Relevance Theory and Evidence Theory,so the lightning risk assessment model of transmission lines driven by data value is constructed.The validity of the method is verified by the actual operation data of 2016 and 2017.An approach of transmission lines trip-out caused by floaters is proposed based on Joint Probability Density Function(JPDF)of extreme wind speed and direction.The spatiotemporal distribution characteristics of floating faults are analyzed based on the research sample of GZ grid.And the calculation method of transmission lines trip-out rate caused by floaters is proposed.At the same time,the effectiveness of the method is verified by using the actual operational data of GZ power grid in 2016.A state evaluation and fault prediction system for overhead transmission lines based on big data platform is proposed.The key information extraction and data interaction methods are summarized,and the knowledge modeling method of overhead transmission lines is studied.An association constraint knowledge base of overhead lines is established based on an example of GZ power grid.A state evaluation and fault prediction system for overhead transmission lines based on big data platform is proposed.The results of real-time fault prediction and assessment for transmission lines are analyzed,which may help optimize the operation of power network and the maintenance of transmission lines.