Hidden failure prediction and treatment method for overhead transmission line

Abstract

The invention relates to a power system, in particular to a hidden failure prediction and treatment method for an overhead transmission line. By means of the method based on a small-world network layer and Bayesian network mixed recommendation network, the overhead transmission line is taken as the object to establish a small-world network layer and Bayesian network mixed recommendation network based mathematical model for evaluating the operation state of the overhead transmission line, hidden failures of the overhead transmission line are predicted effectively and accurately by means of high clustering performance of small-world networks and a bidirectional reasoning technology of the Bayesian network, a failure treatment method can be found out timely when a hidden failure exists, and health warning of the overhead transmission line is guaranteed reliably.

Description

technical field [0001] The invention relates to an electric power system, in particular to a method for predicting and processing an invisible fault of an overhead transmission line. Background technique [0002] As a huge system for producing, transmitting and supplying electric power, the power system is closely related to social and economic operation and people's daily life, and power outages have far-reaching impacts. There are many irrationalities in the regular maintenance system that is consistently implemented in traditional troubleshooting, which not only causes unnecessary power outages or repeated power outages of equipment, but also causes a lot of waste of manpower and financial resources, and seriously affects the improvement of the reliability level of power facilities. Multiple repeated power outages: For example, stop once to do substation work (involvement of the line), and stop again to do line work (involvement of the substation equipment). The reliabili...

AI Technical Summary

Problems solved by technology

Due to the various operating states in the operation of overhead transmission lines, which have great randomness and uncertainty, most traditional state assessment methods are difficult to describe polymorphic events, and the state assessment is only divided into normal and fault analysis; In terms of evaluation efficiency, evaluation accuracy and evaluation scale, various traditional evaluation algorithms are quite different

For the evaluation process of transmission line operating status, the traditional method can only accurately give the weak link of each load point, but generally cannot give the position of each component or a certain component in the reliability of the entire system, and The calculation model is complex, and the calculation workload increases exponentially with the system scale. It is impossible to quickly find the original components that cause invisible faults and deal with invisible faults in a timely manner.

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